Environmental Enrichment (EE) is an animal housing environment that is used to reveal mechanisms that underlie the connections between lifestyle, stress, and disease. This protocol describes a procedure that uses a mouse model of colon tumorigenesis and EE to specifically define alterations in microbiota biodiversity that may impact animal mortality.
Several recent studies have illustrated the beneficial effects of living in an enriched environment on improving human disease. In mice, environmental enrichment (EE) reduces tumorigenesis by activating the mouse immune system, or affects tumor bearing animal survival by stimulating the wound repair response, including improved microbiome diversity, in the tumor microenvironment. Provided here is a detailed procedure to assess the effects of environmental enrichment on the biodiversity of the microbiome in a mouse colon tumor model. Precautions regarding animal breeding and considerations for animal genotype and mouse colony integration are described, all of which ultimately affect microbial biodiversity. Heeding these precautions may allow more uniform microbiome transmission, and consequently will alleviate non-treatment dependent effects that can confound study findings. Further, in this procedure, microbiota changes are characterized using 16S rDNA sequencing of DNA isolated from stool collected from the distal colon following long-term environmental enrichment. Gut microbiota imbalance is associated with the pathogenesis of inflammatory bowel disease and colon cancer, but also of obesity and diabetes among others. Importantly, this protocol for EE and microbiome analysis can be utilized to study the role of microbiome pathogenesis across a variety of diseases where robust mouse models exist that can recapitulate human disease.
Environmental enrichment (EE) studies utilize complex housing parameters to affect social stimulation (large housing cages, larger groups of animals), cognitive stimulation (huts, tunnels, nesting materials, platforms) and physical activity (running wheels). EE has been utilized by many labs to understand the effects of increased activity and improved social and cognitive interactions on disease initiation and progression using a wide array of mouse models, including barbering induced alopecia, Alzheimer's disease, Rett syndrome, and several tumor and digestive disease models1,2,3,4,5,6.
Several mouse models have been developed to study colon tumorigenesis in mice. Perhaps the most well-defined model is the ApcMin mouse. The ApcMin mouse was developed in the laboratory of William Dove in 19907, and has been used as a mouse model of mutations in the APC gene that are commonly associated with human colorectal cancer. In contrast to humans harboring APC mutations, ApcMin mice primarily develop small intestinal tumors, with very rare occurrence of colon tumors. However, a Tcf4Het allele with a single knockin-knockout heterozygous mutation in Tcf4, vastly increases colon tumorigenesis when combined with the ApcMin allele8. Recently, this mouse model of colon tumorigenesis has been used to determine the effects of EE on colon tumorigenesis6. In the Bice et al. study, the physiological and phenotypic effects of EE on males and females of four different mouse lines (wild-type (WT), Tcf4Het/+ Apc+/+, Tcf4+/+ ApcMin/+, and Tcf4Het/+ApcMin/+)) were defined. Perhaps the most interesting finding was that EE significantly increases the lifespan of both male and female colon tumor-bearing animals. This demonstrated that EE may reduce at least some of the symptoms associated with colon tumorigenesis, and improve animal health. Remarkably, this improved lifespan in males is not a direct result of reduced tumorigenesis, and instead was linked to the initiation of a tumor wound healing response, including improved microbiome biodiversity6.
Several EE specific studies have been published with interesting results. However, from a technical standpoint, important results are often not translatable to other laboratories. Maintaining identical EE methodologies between different laboratories is an incredibly complex issue, not only due to enrichment devices and housing used, but also bedding, food, ventilation, breeding, genetics, activity in the room, and animal protocol requirements, among others9,10,11. One example is animal integration, where animals must be stably integrated into the mouse colony, therefore normalizing genetic background and diet composition, to avoid non-treatment related effects. Further, many EE studies have been completed prior to the realization of the importance of the microbiome in disease, and the way that common mouse husbandry practices can affect the composition of the gut microbiome10,12.
Breeding strategy and animal placement in EE can increase stress if not performed properly. Since EE studies utilize large numbers of both male and female animals and multiple genotypes, experimental setup can be difficult given the requirement for animals from several litters to be combined. Therefore, a breeding and weaning strategy was developed to allow for combining of weaned animals of the correct genotype from different litters. The primary rationale for this was to normalize the microbiota among litters and to reduce stress when animals were moved to the experimental environment. The microbiome was transmitted from the dam10. To provide microbial diversity to the colony, females were purchased from Jackson Labs and integrated into the colony for one month before the experiment began9,10,12. To further normalize microbiome biodiversity between animals, females were co-housed prior to breeding. Following breeding, communal housing during rearing and the ability to escape nursing pups improved the stress levels of maternal care13,14, possibly furthering microbiome normalization. To prevent non-EE related effects on the microbiome, this communal housing of all experimental animals prevented fighting and additional stress that occurred when combining several males from different litters into one experimental cage. Finally, equal numbers of animals of all genotypes were included in the cages. This provided the opportunity for improved microbiota biodiversity across genotypes, and removed the contribution of coprophagia (the animal's tendency to consume stool) or possible genotype-specific behavioral differences to the overall study.
This protocol provides a strategy that expands previous EE studies to include known aspects of microbiome research, including microbiota transmission and animal colony integration for microbiota normalization, to enable more uniform microbiome populations between experimental animals. Heeding these precautions is essential due to the ability of non-treatment related microbiota differences to confound study findings. Eliminating non-EE related microbiota changes will enable researchers to specifically define the role of EE on microbiota composition during disease development and progression.
All methods described here were performed in accordance with protocols approved by the Institutional Animal Care and Use Committee (IACUC) at the University of Utah.
1. Experimental Design and EE and Control Cage Setup
Note: For reference, an outline of the experimental design is illustrated (Figure 1).
2. Stool Collection at 16 Weeks of Age
3. Genomic DNA Isolation from Stool
Note: Utilize a commercial kit to isolate microbial DNA from stool following a stool pathogen detection protocol. Remove samples directly for the -80 ˚C freezer and store on dry ice while weighing.
4. DNA Concentration Determination and Sample Preparation for PCR
Note: Utilize a fluorometer and a commercially available dsDNA fluorescent assay to determine genomic DNA concentration in each sample (see Table of Materials). The fluorescent dye must bind double stranded DNA specifically.
5. Design Primers to the 16S Desired V Regions
6. Amplicon PCR to Amplify the V Region(s) with Overhang Adapter Sequences Attached 22
7. PCR Cleanup Using Magnetic Beads 22
8. Preparation of a Plate Scheme for Index PCR
Note: To generate a V1-V3 library, a second PCR was performed with an index kit (see Table of Materials). A default indexing scheme was used to map out unique dual index combinations for each sample (Figure 3B and 23).
9. Perform Index PCR to Attach Barcodes to the Adaptor Sequences as Described 22 .
10. Purify Final PCR Library
Note: This PCR clean-up is identical to step 7 above, and uses magnetic beads to perform PCR Clean-Up of the index PCR22.
11. Quantify, Normalize, and Pool the Indexed Libraries for Sequencing
12. Sequence the Library using a Next Generation Sequencing System and Parse the Data
13. Analyze Sequenced Data from the 16S Amplicon Library
Note: This step is performed as described in Bice et al., 20176.
Several studies have demonstrated that the practice of mind-body medicine improves health outcomes. Similarly, in mice, environmental enrichment improves outcomes including improved lifespan and tumor wound repair6. Therefore, an EE procedure was developed with the aim of defining the role of microbiota in this phenotype while first normalizing the microbiome prior to the initiation of the experiment (Figure 1). Importantly, all breeding animals are integrated into the mouse colony for at least one month prior to the commencement of breeding, and newborn pups are co-housed with mothers in one large cage to normalize microbiota transmission prior to the experiment. When animals are between 21 and 28 days old, equal numbers of each genotype are weaned into their respective housing, either EE or NE environments (Figure 2A). At 16 weeks, stool from all animals is collected and homogenized (Figure 2B), followed by bacterial DNA isolation. Finally, 16S amplicons are amplified from stool microbiome DNA and barcode indexed to allow for sequencing of all microbiome libraries simultaneously (Figure 3). The unique sequences identified in WT and tumor bearing animals in both NE and EE conditions are shown in Figure 4. Interestingly, EE does not improve biodiversity in WT animals, but vastly increases biodiversity in tumor bearing animals (Figure 4), demonstrating that this method allows biodiversity improvements. This increase in biodiversity can be attributed to the increased presence of the phylum Proteobacteria, with significant increases in the classes Alphaproteobacteria and Betaproteobacteria, and decreases in pathogenic Gammaproteobacteria (Figure 5; Supplemental Table 1). The largest increase is in the Betaproteobacteria class is the genus Sutterella, a likely commensal involved in secreted IgA degradation (Figure 6, also see38).
Figure 1: A representation of the experimental timeline. The short bands represent 7-day windows, as most of the protocol is accomplished in 7-day increments. This also aids in visualization of the range of pup ages across the experiment. Please click here to view a larger version of this figure.
Figure 2: EE and NE housing conditions and stool homogenates (as described in the protocol). Please click here to view a larger version of this figure.
Figure 3: 16S Microbiome Library Preparation. (A) Unpurified PCR amplicon products derived from stool genomic DNA.(B) Indexing plate graphic designed as per the software used (Table of Materials, 23). The dual index combinations, I7 (Index 1; Row) and I5 (Index 2; Column), are shown for each sample. Each index is 8 bp in length. The sample numbers refer to the respective mouse numbers from EE studies. (C) Unpurified Index PCR products. (D) Quality analysis of final purified and pooled 16S libraries. (A,C,D) Black arrows denote 550 bp amplicon and 668 bp indexed library. Red arrows denote non-specific products that are eliminated following purification as shown in D. Upper and Lower markers are size markers added to the sample for size reference. Please click here to view a larger version of this figure.
Figure 4: Changes in alpha diversity following EE of Tcf4Het/+ ApcMin/+ animals. Alpha diversity of WT and Tcf4Het/+ ApcMin/+ tumor bearing animals. At 20,000 reads, NE and EE of WT, p=0.64 and NE and EE of Tcf4Het/+ ApcMin/+, p=0.03 using two-sample t-test with Welch correction. Adapted from Bice et al., 20176. Please click here to view a larger version of this figure.
Figure 5: EE-mediated changes following EE of Tcf4Het/+ ApcMin/+ animals. R-ggplot2 generated box-whisker plots denoting changes in abundance of the phylum Proteobacteria and classes Alphaproteobacteria, Betaproteobacteria, and Gammaproteobacteria. Outliers are noted as circles. **p=0.005 using two-sample t-tests with Welch correction. Error bars calculated using standard error of the mean (SEM). Adapted from Bice et al., 20176. Please click here to view a larger version of this figure.
Figure 6: Changes in the relative abundance of Sutterella following EE of Tcf4Het/+ ApcMin/+ animals. Outliers are noted as circles. **p=0.005 using two-sample t-test with Welch correction. Error bars calculated using SEM. Adapted from Bice et al., 20176. Please click here to view a larger version of this figure.
Supplemental Table 1: Classification of Bacteria Isolated from Stool Collected from NE and EE mice. Classification across genotypes at the (A) Phylum, (B) Class, (C) Order, (D) Family, and (E) Genus level. Comparisons of NE and EE of the same genotype or WT to Tcf4Het/+ ApcMin/+. P-values are calculated using a two-sample t-test with Welch correction. Adapted from Bice et al., 20176. Please click here to download this file.
Item | Total Area (inch2) | Total Area (cm2) | Cage Size (inch) L x W x H | Cage Size (cm) L x W x H |
One Control Cage (NE) | 68.25 | 440.32 | 10.5 x 6.5 x 5.5 | 26.67 x 16.51 x 13.97 |
One Large Cage (EE) | 264.36 | 1706.32 | 13.87 x 19.06 x 7.75 | 35.24 x 48.42 x 19.69 |
Two Large Cages (EE) | 528.72 | 3412.64 | 2@ 13.87 x 19.06 x 7.75 | 2@ 35.24 x 48.42 x 19.69 |
Two Platforms (EE) | 93 | 600 | 2@ 11.8 x 3.94 x 2.95 | 2@ 30 x 10 x 7.5 |
Two Large Cages with Two Platforms (EE) | 621.72 | 4013 | 2@ 13.87 x 19.06 x 7.75 + 2@ 11.8 x 3.94 x 2.95 | 2@ 35.24 x 48.42 x 19.69 + 2@ 30 x 10 x 7.5 |
Table 1: EE and NE Cage sizes and Floor Space.
Animals Allowed in Cage | Required Inches Squared Per Animal | EE Cage Area (Inches2) | Total Animals allowed |
Up to 25 | 12 | 622 in2 (4013 cm2) | up to 25 |
25+ | 15 | 622 in2 (4013 cm2) | up to 41 |
Female with Litter | 51 | 622 in2 (4013 cm2) | up to 12 |
Table 2: Allowed Numbers of Animals in Cages Based on Floor Space15.
Amplicon PCR Primers | |||
Forward | 5'-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGagagtttgatcMtggctcag-3' | ||
Reverse | 5'- GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGTTACCGCGGCTGCTGGCAC -3' | ||
Locus specific sequences are shown in bold and the non-bold is the overhang adapter sequence. |
Table 3: Amplicon PCR Primers.
Amplicon PCR reaction set up | |
Volume | |
Microbial DNA (5ng/μl) | 2.5 μl |
Forward Primer (1 μM; from step 5.1.2) | 5.0 μl |
Reverse Primer (1 μM; from step 5.1.2) | 5.0 μl |
2X HotStart Ready Mix | 12.5 μl |
Total | 25.0 μl |
Table 4: Amplicon PCR Mix.
Amplicon PCR set up | |
95 °C for 3 minutes | |
25 Cycles of: | |
95 °C for 30 seconds | |
55 °C for 30 seconds | |
72 °C for 60 seconds | |
72 °C for 3 minutes | |
Hold at 4 °C |
Table 5: Amplicon PCR Program Set Up.
Index PCR barcode assembly | |
DNA | 2.5 μl |
Index Primer 1 (N7XX) | 2.5 μl |
Index Primer 2 (S5XX) | 2.5 μl |
2X HotStart Ready Mix | 12.5 μl |
PCR grade water | 5 μl |
Total | 25 μl |
Table 6: Index PCR Mix.
Index PCR Setup | |
95 °C for 3 minutes | |
8 cycles of: | |
95 °C for 30 seconds | |
55 °C for 30 seconds | |
72 °C for 30 seconds | |
72 °C for 5 minutes | |
Hold at 4 °C | |
Store at -20 °C |
Table 7: Index PCR Program Set Up.
Sample Concentration Formula for Pooling |
(DNA concentration in ng/μl) x 106 = Concentration in nM (660 g/mol x average library size) |
An example from this study is: |
(85.2 ng/μl) x 10^6 = 193.3 nM (660 g/ mol x 668 bp) |
Table 8: Formula for Normalizing Before Pooling Samples
This procedure allows for the analysis of microbiota isolated from stool following environmental enrichment of normal or tumor bearing animals. Because these are large experiments which involve breeding to obtain many animals of different sexes and genotypes, normalizing the microbiome between animals prior to commencement of the experiment is essential to avoid non-EE related effects on microbiome biodiversity.
For consistency between NE and EE conditions, the breeding process is conducted to ensure that all mice initially have exposure to the same microbes and, therefore, are expected to have similar microbiome contents. It is possible, and likely, that mouse genotype affects microbiome composition. For this reason, mouse numbers per genotype are maintained between NE and EE conditions to be certain that any animal that is consuming stool will encounter a similar diversity of the microbiome.
Several difficulties are apparent when designing EE experiments. First, the total number of animals needed for the experiments is dependent upon the experimental details, but the total number is also limited by the number of animals allowed in the cage. For example, historical data surrounding mouse survival in a preliminary EE experiment were used to calculate the number of animals to define the mechanism underlying improved survival observed in previous experiments. From this data, a total of 17 animals in the comparison group were required for an 80% power to detect a difference in survival in a two-sided t-test where alpha=0.05. So, 4-5 animals in the control group vs. 20-24 (or up to 41) animals per cage in the experimental group must accommodate a power calculation. Therefore, several control group cages are needed along with the experimental cage. Further, with complex genotypes, it is difficult to obtain sufficient animal numbers of every genotype, which necessitates the large numbers of breeding females required. However, with other models where fewer transgenic differences are present, more animals of the same genotype can be analyzed in this system and fewer breeders are necessary. In the United States, 12 pregnant females can be housed in the 633 in2 of space (Table 2). The issue with this is that as pups get older, they take up more space. Given that male pups are separated from female pups at 14-21 days, an approved exception to the space rule in certain countries may be feasible. Otherwise, male and female pups can be genotyped and selected, and then separated at a younger age with mothers to stay below the maximum mouse numbers. It is essential to gain approval for these studies and to adhere to local rules on space restrictions. Finally, with microbiota, the number of animals needed to detect even small differences in microbiota composition is difficult to calculate a priori. While in this study, significant differences in the microbiota were found with 4 animals per group, it is possible that increasing that number of animals would reveal microbiota that are more variable between EE mice or are only slightly altered by EE.
This method article describes the particular equipment and bedding that are used, which on the surface may not appear essential. However, several non-obvious issues that affect consistency can be encountered and need to be addressed prior to embarking on these very large, expensive, and time-consuming studies. One major issue is cage ventilation. With large numbers of animals in a cage, ventilation becomes an issue, and is an issue that most researchers do not take into account when attempting to provide consistent environments between control and experimental cages. All cages in the described setup are placed in a ventilated cabinet to equalize ventilation across the experimental and control cages. This cannot be accomplished when using cages that do not fit in a ventilated cabinet. Other means to normalize ventilation between experimental and control animals could be tested and consistently applied, but these methods are not explored in this study. Similar consistency issues arise with bedding. In the colon cancer model system used in this study, animals that have digestive disease will ingest certain types of bedding, especially corn cob bedding, leading to digestive blockages and illness. It is important to keep this in mind if the animals are known to ingest bedding when not otherwise occupied, as in the control environment. This phenomenon and the subsequent inconsistent health effects will profoundly affect all data.
The ribosomal 16S gene has been used as a means to study bacterial populations. It contains nine regions that express genetic variability, V1-V9, and interspersed conserved regions that remain relatively unchanged between bacterial species39. The V1-V3 region, in particular, provides the highest probability of species-level identification39. Similar V1-V3 studies on Colorectal Cancer (CRC) and Advanced Colorectal Adenoma found changes in three phyla of interest: Bacteroidetes, Firmicutes, and Proteobacteria21,40,41. It has also been reported that exercise can shift the microbial population and lead to an increase in Firmicutes42. For this reason, this study identified microbiome populations by using V1-V3 primers following a 16S metagenomic library prep protocol22 to potentially define the effects of environmental enrichment on these phyla known to be altered in adenoma and CRC. This procedure can be modified to amplify and sequence other variable regions of the 16S rRNA genes. One way is to use Probe Match to understand the phylogenetic classification of microbiota present in the sample that will be identified by the probe. In this way, probes can be targeted to specifically define and phylogenetically classify microbes of interest. This allows a different characterization of the microbiota present in the stool samples, and may reveal additional EE-dependent alterations in the microbiome of tumor bearing mice that may affect disease progression.
Using this procedure, the genus Sutterella was identified as the most altered genus following EE of tumor bearing animals. This procedure can be adapted to accommodate studies that utilize any method meant to analyze the effects of a perturbation on microbiome composition in genetically modified models of human disease. For example, in place of EE, mice could be inoculated with Sutterella to define whether Sutterella inoculation is sufficient to increase microbial biodiversity and wound repair in 16-week-old male tumor bearing animals.
Undoubtedly, the most unique aspect of this protocol is the concern over normalizing microbiota prior to EE and maintaining microbiome diversity throughout the EE studies. Since microbiome studies are continually improving, it is likely that more robust methods for characterizing the microbiome will arise, and the microbiome characterization in this protocol will become obsolete. For example, with the current study, the probes used to amplify the 16S rRNA have bias, depending on the probes that are chosen, and do not characterize all of the bacteria present in the microbiome. While the methods used to survey and characterize the microbiome will undoubtedly improve, the basic foundation of designing and running EE experiments while keeping normalization of the microbiome in mind will remain an essential facet of EE experiments.
The authors have nothing to disclose.
We thank B. Dalley in the University of Utah Genomics core for library sequencing, and K. Boucher in the University of Utah Biostatistics core for statistical advice, and access to these technical cores supported by National Cancer Institute award P30 CA042014. The project described was supported by the National Cancer Institute Grants P01 CA073992 and K01 CA128891 and the Huntsman Cancer Foundation.
Teklad Diets/Harlan Labs Chow | Harlan Labs | 3980X | Standard irradiated chow formulated by Dr. Mario Capecchi in collaboration with Harlan Labs. |
Cell-Sorb Plus bedding | Fangman Specialties | 82010 | Autoclave prior to use. |
AIMS Tattooing System For Neonates | AIMS | NEO-9 | https://animalid.com/neonate-rodent-tattoo-identification/32. Other animal grade tattoo systems and inks can be used with similar results including the Aramis Micro Tattoo Kit. |
Zyfone One Cage 2100 AllerZone Mouse Micro-Isolator System Complete with cage, AllerZone filter top and modular diet delivery system | Lab Products | 82120ZF | Each EE cage requires one of each catalog # 82120ZF, 82100ZF, and 82101ZF, as well as two of 82109ZF. Food is only in one side. |
Zyfone One Cage 2100 Life Span Enrichment Device | Lab Products | 82109ZF | Each EE cage requires one of each catalog # 82120ZF, 82100ZF, and 82101ZF, as well as two of 82109ZF. Food is only in one side. |
Zyfone One Cage 2100 Cage 13-7/8" Length X 19-1/16" Width X 7-3/4" Depth | Lab Products | 82100ZF | Each EE cage requires one of each catalog # 82120ZF, 82100ZF, and 82101ZF, as well as two of 82109ZF. Food is only in one side. |
Zyfone One Cage 2100 AllerZone Micro-Isolator filter top | Lab Products | 82101ZF | Each EE cage requires one of each catalog # 82120ZF, 82100ZF, and 82101ZF, as well as two of 82109ZF. Food is only in one side. |
Tunnel | Bio-Serv | K3323 or K3332 | Connect cages together and use for enrichment |
Grommet to connect Tunnel to cages | Fabricated by the University of Utah Machine Shop | n/a | Be certain the material is resistant to chewing and autoclavable |
Fast-track wheel | Bio-Serv | K3250 or K3251 | Use with mouse igloo and floor |
Mouse Igloo | Bio-Serv | K3328, K3570 or K3327 | Use with Fast-track wheel and floor |
Mouse Igloo floor | Bio-Serv | K3244 | Use with mouse Igloo and Fast-Track |
Mouse Hut | Bio-Serv | K3272, K3102 or K3271 | |
Crawl Ball | Bio-Serv | K3330 or K3329 | |
Bio-hut | Bio-Serv | K3352 | Wood pulp hut used for sheltering and nesting |
Adhesive film | VWR | 60941-072 | Use to temporarily cover drilled hole in large cage to prevent mice from escaping |
Laminar Flow Ventilated Rack | Techniplast | Bio-C36 | The cabinet we used in this study is not currently supplied. The Bio-C36 is very similar. |
1.5 mL Microfuge Tube- RNAse and DNAse free | Any supplier | ||
QIAamp DNA Stool MiniKit | Qiagen | 51504 | This kit supplies reagents for 50 DNA preparations. Stool Lysis Buffer=ASL; Guanidinium Chloride Lysis Buffer= AL; Wash Buffer 1 with Guanidinium Chloride= AW1; Wash Buffer 2= AW2; Elution Buffer with EDTA=AE |
Waterbath (capable of heating to 95) | Any supplier | For 94 degree incubation of stool samples to lyse cells. | |
Waterbath (capable of heating to 70 degrees) | Any supplier | For 70 degree incubation of stool samples | |
Ethanol (200 proof) | Sigma Aldrich | E7023 | |
Fluorometer: Qubit | ThermoFisher Scientific | Q33216 | |
Qubit dsDNA broad Range Assay Kit | ThermoFisher Scientific | Q32850 | |
EB Buffer or 10 mM Tris pH 8.5 | Qiagen | 19086 | |
Experiment specific primers | Any Supplier | ||
PCR grade water | Any supplier | ||
2X KAPA HiFi HotStart Ready Mix | Kapa Biosystems | KK2601 | For Amplicon Amplification (1.25 mL allows 100 rxns). |
Agarose for running diagnostic gels | Any supplier | ||
TapeStation High Sensitivity D1000 Screen Tape Trace | Agilent | 5067-5583 | TapeStation or Bioanalyzer instruments are common in Institutional Genomics Cores to analyze library quality . Alternatively a Bioanalyzer DNA1000 Chip (Agilent, 5067-1504) can be used. |
Agencourt AMPure XP Magnetic Beads | Beckman Coulter | A63880 | Magentic beads For PCR cleanup- 5 mL will clean 250 PCR reactions |
Magnetic stand | Life Technologies | AM10027 | |
Library Preparation Guide | Illumina | Illumina. 16S Metagenomic Sequencing Library Preparation: Preparing 16S ribosomal RNA Gene Amplicons for the Illumina MiSeq System. https://support.illumina.com/content/dam/illumina-support/documents/documentation/chemistry_documentation/16s/16s-metagenomic-library-prep-guide-15044223-b.pdf. | |
Unique Dual Indexing | Illumina | Illumina Experiment Manager Software | Freely available at: https://support.illumina.com/sequencing/sequencing_software/experiment_manager/downloads.html |
Nextera XT 96 Index Kit | Illumina | FC-131-1002 | Used to add barcodes to amplicons |
MicroAmp Optical 96-well reaction plate | Applied Biosystems/ThermoFisher | N8010560 | |
TruSeq Index Plate Fixture | Illumina | FC-130-1005 | |
Adhesive clear plate seal | Applied Biosystems /ThermoFisher | 4360954 | Applied Biosystems/ThermoFisher Microamp adhesive film |
Sequencing by MiSeq with v3 reagents and dual 300 bp reads | Illumina | MS-102-3003 | |
PhiX Control Kit | Illumina | FC-110-3001 | |
Proteinase K (600 mAU/ml) | Qiagen | 19131 | Equivalent to 20 mg/ml of proteinase K. Supplied with QiaAmp kit |
Data Analysis Tools | Qiime | QIIME software Tools | Installation may differ based on your system and the QIIME website describes several options (http://qiime.org/install/install.html). For this study, MacQIIME software package 1.9.1 was utilized (compiled by Werner Lab, SUNY, http://www.wernerlab.org/software/macqiime |
Step 13.2. | Qiime | FastQ Join method | (http://code.google.com/p/ea-utils ). For this study Multiple join paired ends was used http://qiime.org/scripts/multiple_join_paired_ends.html. Aronesty, E. ea-utils: Command-line tools for processing biological sequencing data. Expression Analysis, Durham, NC. (2011). |
Step 13.3. | Qiime | De-Novo OTU picking protocol | http://qiime.org/scripts/pick_de_novo_otus.html. |
Step 13.3.1. | Open Taxonomic Units (OTUs) using Uclust | Edgar, R.C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 26 (19), 2460-2461, doi:10.1093/bioinformatics/btq461 (2010). | |
Step 13.3.1. | Pynast | Pynast | Caporaso, J.G. et al. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics. 26 (2), 266-267, doi:10.1093/bioinformatics/btp636 (2010). |
Step 13.3.1. | Pynast | Pynast_Greengenes | DeSantis, T.Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 72 (7), 5069-5072, doi:10.1128/AEM.03006-05 (2006). Greengenes version 13_8 was used in this study |
13.3.1. Note: | Qiime | Multiple Split Libraries | http://qiime.org/scripts/multiple_split_libraries_fastq.html. |
13.3.1. Note: | Qiime | Pick de novo OTUs script | http://qiime.org/scripts/pick_de_novo_otus.html |
Step 13.2.2. | Qiime | Create a mapping file | http://qiime.org/documentation/file_formats.html. |
Step 13.2.2. | Qiime | Validate a mapping file | http://qiime.org/scripts/validate_mapping_file.html. |
Step 13.3.3. | Qiime | Link the OTU to sample description to mapping file | http://qiime.org/scripts/make_otu_network.html. |
Step 13.3.4. | Qiime | Summarize Taxa through plots | http://qiime.org/scripts/summarize_taxa_through_plots.html. |
Step 13.3.5. | Qiime | Biome Summarize table | http://biom-format.org/documentation/summarizing_biom_tables.html In this study, all samples were rarified to 20,000 OTUs followed by analysis using alpha rarefaction script in QIIME. |