Title: DNA extraction protocols cause differences in 16S rRNA amplicon sequencing efficiency but not in community profile composition or structure
Abstract: MicrobiologyOpenVolume 3, Issue 6 p. 910-921 Original ResearchOpen Access DNA extraction protocols cause differences in 16S rRNA amplicon sequencing efficiency but not in community profile composition or structure Benjamin E. R. Rubin, Corresponding Author Benjamin E. R. Rubin Committee on Evolutionary Biology, University of Chicago, Chicago, Illinois Department of Science and Education, Field Museum of Natural History, Chicago, Illinois Correspondence Benjamin E. R. Rubin, Committee on Evolutionary Biology, University of Chicago, Culver Hall 402, 1025 E. 57th Street, Chicago 60637, IL. Tel: (312) 665-7776; Fax: (312) 665-7754; E-mail: [email protected] for more papers by this authorJon G. Sanders, Jon G. Sanders Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MassachusettsSearch for more papers by this authorJarrad Hampton-Marcell, Jarrad Hampton-Marcell Institute of Genomic and Systems Biology, Argonne National Laboratory, Lemont, Illinois Department of Ecology and Evolution, University of Chicago, Chicago, IllinoisSearch for more papers by this authorSarah M. Owens, Sarah M. Owens Institute of Genomic and Systems Biology, Argonne National Laboratory, Lemont, Illinois Computation Institute, University of Chicago, Chicago, IllinoisSearch for more papers by this authorJack A. Gilbert, Jack A. Gilbert Institute of Genomic and Systems Biology, Argonne National Laboratory, Lemont, Illinois Department of Ecology and Evolution, University of Chicago, Chicago, IllinoisSearch for more papers by this authorCorrie S. Moreau, Corrie S. Moreau Department of Science and Education, Field Museum of Natural History, Chicago, IllinoisSearch for more papers by this author Benjamin E. R. Rubin, Corresponding Author Benjamin E. R. Rubin Committee on Evolutionary Biology, University of Chicago, Chicago, Illinois Department of Science and Education, Field Museum of Natural History, Chicago, Illinois Correspondence Benjamin E. R. Rubin, Committee on Evolutionary Biology, University of Chicago, Culver Hall 402, 1025 E. 57th Street, Chicago 60637, IL. Tel: (312) 665-7776; Fax: (312) 665-7754; E-mail: [email protected] for more papers by this authorJon G. Sanders, Jon G. Sanders Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MassachusettsSearch for more papers by this authorJarrad Hampton-Marcell, Jarrad Hampton-Marcell Institute of Genomic and Systems Biology, Argonne National Laboratory, Lemont, Illinois Department of Ecology and Evolution, University of Chicago, Chicago, IllinoisSearch for more papers by this authorSarah M. Owens, Sarah M. Owens Institute of Genomic and Systems Biology, Argonne National Laboratory, Lemont, Illinois Computation Institute, University of Chicago, Chicago, IllinoisSearch for more papers by this authorJack A. Gilbert, Jack A. Gilbert Institute of Genomic and Systems Biology, Argonne National Laboratory, Lemont, Illinois Department of Ecology and Evolution, University of Chicago, Chicago, IllinoisSearch for more papers by this authorCorrie S. Moreau, Corrie S. Moreau Department of Science and Education, Field Museum of Natural History, Chicago, IllinoisSearch for more papers by this author https://doi.org/10.1002/mbo3.216Citations: 67AboutSectionsPDF ToolsExport citationAdd to favoritesTrack citationReprints ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Graphical Abstract In this study, we examine the efficacy of several commonly used protocols for extracting bacterial DNA from ants. While bacterial community composition recovered using Illumina 16S rRNA amplicon sequencing was not detectably biased by any method, the quantity of bacterial DNA varied drastically, reducing the number of samples that could be amplified and sequenced. Exoskeletal pulverization and tissue digestion increased the reliability of extractions, suggesting that these steps be included in any study of insect-associated microorganisms that relies on obtaining microbial DNA from intact body segments. Abstract The recent development of methods applying next-generation sequencing to microbial community characterization has led to the proliferation of these studies in a wide variety of sample types. Yet, variation in the physical properties of environmental samples demands that optimal DNA extraction techniques be explored for each new environment. The microbiota associated with many species of insects offer an extraction challenge as they are frequently surrounded by an armored exoskeleton, inhibiting disruption of the tissues within. In this study, we examine the efficacy of several commonly used protocols for extracting bacterial DNA from ants. While bacterial community composition recovered using Illumina 16S rRNA amplicon sequencing was not detectably biased by any method, the quantity of bacterial DNA varied drastically, reducing the number of samples that could be amplified and sequenced. These results indicate that the concentration necessary for dependable sequencing is around 10,000 copies of target DNA per microliter. Exoskeletal pulverization and tissue digestion increased the reliability of extractions, suggesting that these steps should be included in any study of insect-associated microorganisms that relies on obtaining microbial DNA from intact body segments. Although laboratory and analysis techniques should be standardized across diverse sample types as much as possible, minimal modifications such as these will increase the number of environments in which bacterial communities can be successfully studied. Introduction The reduction in sequencing-associated costs required for analyzing microbial community structure using the 16S rRNA gene has caused a rapid increase in the number and breadth of these studies (Knight et al. 2012). Some of the best-known applications of these techniques to host-associated communities have focused on the human microbiome (Costello et al. 2009; Caporaso et al. 2011; Wu et al. 2011; Schloissnig et al. 2013), but as characterizing novel bacterial communities has become cheaper and easier (Liu et al. 2007; Andersson et al. 2008; Bartram et al. 2011; Caporaso et al. 2012), studies have begun to explore the microbiota of such diverse environments as mammal and honeybee guts (Muegge et al. 2011; Martinson et al. 2012), leaf-cutter ant fungal gardens (Suen et al. 2010; Aylward et al. 2012), marine systems (Gilbert et al. 2012; Gibbons et al. 2013), and even oil plumes (Hazen et al. 2010). The increase in breadth of these investigations has shifted attention toward optimization of sample processing to increase the number of samples that can be examined and directly compared; sequencing is no longer the bottleneck it once was. Sharing of standard operating procedures for sample preparation and data analysis has been an invaluable part of these efforts, making this area of study available to a broad array of researchers (e.g., Caporaso et al. 2012; Engel et al. 2013; Kozich et al. 2013; Schloss et al. 2011; http://www.earthmicrobiome.org/emp-standard-protocols/; http://www.mothur.org/wiki/Analysis_examples; http://qiime.org/tutorials/index.html). Optimization of DNA extraction methods is of particular interest when developing protocols as this is among the first steps in the analysis of microbial diversity, and therefore can have a significant influence on the structure and diversity of the recovered community profile (de Lipthay et al. 2004; Carrigg et al. 2007; Feinstein et al. 2009; Willner et al. 2012). Indeed, certain protocols can even systematically introduce contaminants (Willner et al. 2012). To help avoid problems of extraction bias, recent initiatives to investigate microbiomes on a large scale, including the Earth Microbiome Project (EMP) (Gilbert et al. 2010a,b, 2011) and the Human Microbiome Project (Turnbaugh et al. 2007; The NIH HMP Working Group 2009; Knight et al. 2012), have placed a premium on standardization of sample handling and processing techniques. This standardization removes biases associated with different extraction protocols, PCR reactions, and sequencing platforms; however, the exclusion of those samples not compatible with the chosen standards limits the communities that can be examined. Insect-associated bacterial communities have recently proven to be fruitful subjects of study (e.g., Martinson et al. 2012; Jones et al. 2013; Kautz et al. 2013a,b; Hu et al. 2014; Jing et al. 2014; Sanders et al. 2014). Despite interest in making broad comparisons across many taxa, standard methods for DNA extraction have not been established in this group (Russell et al. 2009; Colman et al. 2012; Jones et al. 2013). Rigid exoskeletons offer an additional challenge to DNA extraction often not considered when designing general protocols. While many microbiome studies examine animal feces, similar materials are often difficult to obtain from insects. Thus, dissected gut tissues, abdominal segments containing the entire digestive tract, or whole insects have been used instead (Jones et al. 2013; Kautz et al. 2013b). Here, we use several ant species as focal organisms to compare the quantity of bacterial DNA obtained from standard methods of nucleic acid extraction. We then examine how each technique affects the microbial community structure and composition using 16S rRNA amplicon sequencing of the V4 region following the EMP standard protocols (http://www.earthmicrobiome.org/emp-standard-protocols/16s/). Ants are an ideal group in which to study these issues as closely related and communally living individuals are easy to collect in large numbers and many species possess armored exoskeletons. We propose that the methodological comparison provided here is an excellent proxy for insects in general. Experimental Procedures DNA extraction We compare four DNA extraction protocols in this study: (1) Phenol–chloroform (Sigma-Aldrich Co. LLC, Saint Louis, MO), (2) the Qiagen DNeasy Blood & Tissue Kit (Qiagen Inc., Valencia, CA), (3) the PowerSoil DNA Isolation Kit (MO BIO Laboratories, Carlsbad, CA), and (4) the PowerSoil DNA Isolation Kit with the addition of a tissue homogenization and digestion step (modified PowerSoil). Following standard protocols, the phenol–chloroform extractions included a tissue homogenization step using a Qiagen TissueLyser. Tissues were pulverized dry with Qiagen tungsten carbide beads for 20 sec at 30 beats per second. Subsequently, 250 μL of buffer A (200 mmol/L Tris-HCl pH 8.8, 60 mmol/L NaCl, 10 mmol/L EDTA [ethylenediaminetetraacetic acid], 0.15 mmol/L spermine, and 0.15 mmol/L spermidine) were added to the homogenized ants and nucleases were inactivated by a 15 min incubation at 65°C. Then, 250 μL buffer B (200 mmol/L Tris-HCl pH 8.8, 30 mmol/L EDTA, and 2% SDS) were added and samples were incubated for 10 min at 65°C. To fully digest ant tissues, 100 μg of proteinase K were added, and the samples were again incubated at 56°C for at least 1 h. Two phenol–chloroform (phenol/chloroform/isoamyl alcohol, 25:24:1, pH 8.0) washes and a third chloroform wash to remove residual phenol, were performed. DNA was precipitated with 50 μL of 5 mol/L NaCl and 1 mL of ice-cold 100% ethanol. Pellets were washed with 70% ethanol and resuspended in 50 μL of Tris-EDTA (TE). The Qiagen extractions included an identical tissue homogenization step to the phenol–chloroform approach, as suggested in the manufacturer's protocol. All subsequent steps were completed using the standard manufacturer's protocols, including overnight proteinase K digestion. Elutions were done with 50 μL buffer AE. The standard PowerSoil extraction was performed according to the manufacturer's protocol with the addition of a 20 min incubation at 65°C after addition of solution C1, as suggested by the EMP (http://www.earthmicrobiome.org/emp-standard-protocols/16s/). Samples were eluted in 50 μL of solution C6. Explicit tissue homogenization and enzymatic digestion steps are absent from the standard PowerSoil protocol. These steps were incorporated into extraction method four (modified PowerSoil) by first homogenizing samples on the TissueLyser as in the phenol–chlorofom and Qiagen methods, and then incubating at 56°C overnight in 500 μL PowerSoil bead solution, 60 μL solution C1, and 100 μg proteinase K. The digested samples were added to the PowerBead tubes and the extractions completed using the entire PowerSoil protocol. Again, all samples were eluted in 50 μL of solution C6. Total DNA concentrations of all samples were calculated on a Qubit fluorometer with the dsDNA High Sensitivity Assay Kit (Life Technologies Corp., Carlsbad, CA) using 5 μL of extract. Samples below the detection limit (<0.20 ng/μL) were assigned values of 0.20 ng/μL for analysis and visualization. Insect samples By comparing extraction protocols performed on different individuals, we introduced additional biological variation and reduced the sensitivity of our study to find minor differences in community structure due to biases in DNA extraction methodology. Subjecting the same sample to different extraction protocols would be more sensitive to detecting potential methodological influences, but, given that the influence of sample homogenization was part of what we aimed to test, this was not possible. However, due to their shared nests, close relatedness, and social food sharing through oral–oral trophallaxis, ants from the same colony are generally expected to host similar bacterial communities (Koch and Schmid-Hempel 2011; Koch et al. 2012, 2013; Hu et al. 2014). We utilized this intra-colony similarity for our study, assuming that bacterial communities would be similar enough within colonies to reveal influences of extraction methodology. We used specimens from four species of ants: Pseudomyrmex flavicornis, Pseudomyrmex nigrocinctus, Cephalotes varians, and Crematogaster rochai. Ward (1993) and Longino (2003; http://academic.evergreen.edu/projects/ants/AntsofCostaRica.html) were used for ant species identification. All Pseudomyrmex and Crematogaster specimens were collected in June of 2012 at Santa Rosa Biological Station in the Área de Conservación Guanacaste in northwestern Costa Rica. Cephalotes varians specimens were collected from the Florida Keys between 2009 and 2011. Samples were stored in 95% ethanol until DNA extraction, which has been shown to be appropriate for preserving ant and ant-associated bacterial DNA (Moreau et al. 2013). All ants were surface sterilized in 5% bleach (0.25% weight/volume sodium hypochlorite solution) for one minute as in Sanders et al. (2014) and rinsed once with ddH2O before abdomens (metasomas) were removed from adult ants and extractions performed on just this part of the body. Larvae were extracted whole after surface sterilization. For each of the extraction protocols, there were four types of ant material used: (1) a single adult abdomen, (2) three pooled adult abdomens, (3) a single larva, and (4) three pooled larvae. Larvae were not available for all colonies. All combinations of protocol and ant material were extracted from three colonies of each of the Pseudomyrmex species. DNA was extracted from adults only for a fourth colony of each Pseudomyrmex species, for four colonies of Crematogaster rochai, and for three colonies of Cephalotes varians. For one of the Cephalotes varians colonies, two extractions of each type were performed. In total, either eight or 16 extractions were performed for each colony. To serve as negative controls, three blank extractions with no insect material were also performed for each extraction protocol. Overall, 32 or 56 extractions were performed per ant species and 47 extractions were performed for each methodology, for a total of 188 DNA extractions. The same number and type of individuals from the same colonies were extracted using each protocol. A list of samples extracted per protocol is shown in Table 1. Note that individuals from particular colonies and life stages were paired across protocols for statistical analyses. All samples included are shown in Table S1. Table 1. List of all extractions conducted for each extraction methodology. All sample characteristics were exactly replicated across extraction protocols 47 extractions per protocol Pseudomyrmex nigrocinctus 3 colonies each with 4 sample types 1 individual adult 1 individual larva 1 pool of 3 adults 1 pool of 3 larvae 1 colony with 2 sample types 1 individual adult 1 individual larva Pseudomyrmex flavicornis 3 colonies each with 4 sample types 1 individual adult 1 individual larva 1 pool of 3 adults 1 pool of 3 larvae 1 colony with 2 sample types 1 individual adult 1 individual larva Cephalotes varians 2 colonies each with 2 sample types 1 individual adult 1 individual larva 1 colony with 4 sample types 2 individual adults 2 individual larvae Crematogaster rochai 4 colonies with 2 sample types 1 individual adult 1 individual larva 3 blanks Bacterial quantification We measured the amount of bacterial DNA present in extracts with quantitative PCRs (qPCRs) of the bacterial 16S rRNA gene. We used the 515f (5′-GTGCCAGCMGCCGCGGTAA) and 806r (5′ - GGACTACHVGGGTWTCTAAT) universal bacterial primers of the EMP to amplify the 16S rRNA gene from all bacteria and archaea present (http://www.earthmicrobiome.org/emp-standard-protocols/16s/). All qPCRs were performed on a CFX Connect Real-Time System (Bio-Rad, Hercules, CA) using SsoAdvanced 2X SYBR green supermix (Bio-Rad) and 2 μL of DNA extract. Standard curves were created from serial dilutions of linearized plasmid containing inserts of the E. coli 16S rRNA gene. Melt curves were used to confirm the absence of qPCR primer dimers and we confirmed that amplification resulted from ant-associated communities and not contaminating DNA by comparing to no template controls. All samples were analyzed in triplicate and each standard dilution was included in triplicate in each qPCR reaction. The resulting triplicate quantities for each sample were averaged before calculating the number of bacterial 16S rRNA gene copies per microliter of DNA solution. These numbers were log10 transformed for all statistical analyses. We used repeated measures analysis of variance (ANOVA) to assess the effect of extraction methodology on number of 16S rRNA gene copies. For this analysis, treatments were considered to be those samples from the same species, life stages, number of individuals, and colonies and samples from the same treatments were paired across extraction methodologies. Therefore, extraction methodology was the single independent variable and we measured the number of 16S rRNA gene copies in response to this variable. We also performed multifactor ANOVA to examine the effects of all other variables (species, colony, life stage, number of individuals) and their interactions. We confirmed our conclusions from these tests within each species using two-way ANOVAs with two independent variables, extraction method, and a sample-type variable that included life stage and number of individuals. Bonferroni-corrected pairwise t-tests were used to compare extraction kits, again pairing samples on the basis of all other variables. Differences in the number of bacterial 16S rRNA gene copies between life stages and number of individuals included in extractions were also determined by Bonferroni-corrected pairwise t-tests. The normality of the distribution of differences between all pairs of samples was confirmed prior to all statistical analyses. Bacterial community analysis Bacterial 16S rRNA gene V4 sequencing was done on an Illumina HiSeq2000 (Illumina Inc., San Diego, CA) by the EMP following their standard sequencing protocols (Caporaso et al. 2012; http://www.earthmicrobiome.org/emp-standard-protocols/16s/) with the same primers used for qPCRs above with the addition of the necessary Illumina adapter sequences (515f: 5′ - AATGATACGGCGACCACCGAGATCTACACTATGGTAATTGTGTGCCAGCMGCCGCGGTAA, 806r: 5′ - CAAGCAGAAGACGGCATACGAGATAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT; X's indicate barcode bases). We used forward reads for community comparisons, but the barcodes associated with the reverse primers were also sequenced so that samples could be demultiplexed. Samples for which sequencing yielded no or very few (<500) raw reads were considered sequencing failures. QIIME (Caporaso et al. 2010b) was used for most analyses of the resulting data. Sequence data were simultaneously demultiplexed and quality filtered using default parameters. We called Operational Taxonomic Units (OTUs) with UCLUST open-reference 97% similarity clustering against the May 2013 release of the Greengenes database (DeSantis et al. 2006). OTUs represented by single sequences were excluded and representative sequences from the remaining non-reference OTUs were aligned against the Greengenes core set using PyNAST (Caporaso et al. 2010a). OTUs represented by sequences that failed to align frequently represented co-amplified ant 18S rRNA gene sequences and were also excluded from further analyses. The remaining sequences were then inserted into the Greengenes reference tree using ParsInsert (http://parsinsert.sourceforge.net/). Rarefaction curves were generated using mothur version 1.33.3 (Schloss et al. 2009). The OTU table was rarefied to a depth of 44,000 for further analyses. Alpha diversity was quantified using richness, Shannon diversity, and evenness as implemented in QIIME's equitability metric. Nonparametric t-tests with 1000 Monte Carlo permutations were used to compare alpha diversities between groups of samples. Comparisons of alpha diversities were based on averages of 1000 rarefactions. Random forests supervised learning classification as implemented in QIIME (Knights et al. 2011) as well as ANOSIM and PERMANOVA tests of Bray-Curtis, weighted UniFrac, and unweighted UniFrac beta diversity metrics were used to compare community level differences between treatments. Individual OTUs that appeared in at least 25% of samples were examined for relative abundance differences between treatments using ANOVA with Bonferroni correction. Community level differences were visualized using Principal Coordinates Analysis (PCoA) plots. The proportions of samples that were successfully sequenced using each extraction methodology were compared with χ2 tests. We used separate t-tests to examine the relationship between sequencing success and both DNA concentration and bacterial 16S rRNA gene copies. The relationships between the number of sequence reads obtained from a sample and both the total DNA concentration and number of bacterial 16S rRNA gene copies were examined with Pearson's correlations. Results Bacterial quantification The effect of extraction methodology on the concentration of the bacterial 16S rRNA gene was highly significant (Repeated measures ANOVA, F = 61.5, P < 2 × 10−16; Fig. 1A). Multifactor ANOVA revealed that all other variables and several interactions were also significant (Table 2). Two-way ANOVAs on samples within each species confirmed that both extraction methodology and life stage were significantly associated with the number of 16S rRNA gene copies recovered in all species but Crematogaster rochai (Table S2). Qiagen, phenol–chloroform, and modified PowerSoil all had significantly larger numbers of 16S rRNA gene copies than the unmodified PowerSoil protocol (Bonferroni-corrected paired t-tests, P < 4.0 × 10−10; Figs. 1A, 2A, 3). Concordantly, sequencing success was significantly associated with extraction methodology (χ2-test, χ = 24.4, P = 2.0 × 10−5; Fig. 2A) and both the number of copies of the bacterial 16S rRNA gene found in each extraction (t-test, t = 14.04, P < 2.2 × 10−16; Fig. 2A) and the DNA concentration of the sample (t-test, t = 3.37, P = 0.001; Fig. 2B). In adult samples, pooling was an effective strategy for boosting 16S rRNA gene concentration as the extractions with three individuals had significantly greater quantities of bacterial DNA than single individual extractions (paired t-test, t = 4.6, P = 1.9 × 10−5; Fig. 3), but this was not the case in larvae (t = 1.45, P = 0.16; Fig. 3). In general, adult abdomens hosted significantly more bacterial DNA than whole larvae overall (paired t-test, t = 3.20, P = 0.003; Fig. 3), but not when using the unmodified PowerSoil extractions (Fig. 3). Total DNA concentration including both ant and microbe DNA was greatest in phenol–chloroform and Qiagen extractions and lowest in PowerSoil extractions (Bonferroni-corrected paired t-tests, P < 1 × 10−4; Fig. 1B). The number of copies of the bacterial 16S rRNA gene was significantly correlated with DNA concentration (Pearson's correlation, t = 6.70, P = 2.8 × 10−10; Fig. 2B), suggesting that effective extraction techniques are useful for all types of DNA present in these samples. The number of copies of the bacterial 16S rRNA gene was also correlated with the number of reads sequenced from each sample (Pearson's correlation, t = 3.88, P = 2.4 × 10−4; Fig. 2C). Different host species had significantly different ratios of bacterial 16S rRNA gene copy number to total DNA quantity (ANOVA, F = 59.21, P < 2 × 10−16), implying that overall bacterial load differs between these species (Fig. 3). Table 2. Multifactor ANOVA on qPCR results including all interaction terms Factor df F P value Kit 3 113.4 <2 × 10−16a Species 3 198.0 <2 × 10−16a Colony 11 10.8 2.4 × 10−12a #Individuals 1 22.0 9.8 × 10−6a Life stage 1 22.8 7.0 × 10−6a Kit:Species 9 4.6 6.0 × 10−5a Kit:Colony 33 2.4 0.0008a Kit:#Individuals 3 1.0 0.39 Kit:Life stage 3 15.8 2.5 × 10−8a Species:#Individuals 3 2.1 0.11 Species:Life stage 1 0.78 0.38 Colony:#Individuals 11 0.54 0.87 Colony:Life stage 4 4.7 0.002a a Significant effect (P < 0.01). Figure 1Open in figure viewerPowerPoint Measures of success for each bacterial extraction protocol (Phenol–chloroform: PC; Qiagen DNeasy Blood & Tissue Kit: Qiagen; PowerSoil DNA Isolation Kit: PowerSoil; and PowerSoil DNA Isolation Kit with the addition of a tissue homogenization and digestion step: modified PowerSoil). (A) Mean ± SE copies of bacterial 16S rRNA gene/μL. (B) Mean ± SE total DNA concentrations (ng/μL). Letters above bars show significant differences as determined by paired t-tests (P < 1 × 10−4). Figure 2Open in figure viewerPowerPoint (A) Bacterial 16S rRNA gene concentrations (rRNA/μL) recovered from all extracted samples by protocol (following notation as in Fig. 1). Colors correspond to ant species. Note the distribution of samples that we sequenced (filled circles) and that we failed to sequence (empty circles). (B) Total DNA concentration (log10(ng/μL)) versus the concentration of the bacterial 16S rRNA gene (rRNA/μL). (C) Correlation between concentration of the bacterial 16S rRNA gene (rRNA/μL) and the number of reads (×105) recovered for samples that we were able to sequence. Figure 3Open in figure viewerPowerPoint Mean ± SE concentration of the bacterial 16S rRNA gene (rRNA/μL) by sample life stage and number of individuals for each species (following notation as in Fig. 1). Bacterial community sequencing We obtained a total of 11,842,139 sequence reads for our 188 samples and 10,246,746 of these passed quality filtering. However, 116 samples were assigned between zero and 331 sequences (Table S1). These samples were considered sequencing failures and were excluded from analyses. Between 44,830 and 419,430 (median = 144,625) sequences were assigned to the remaining 72 samples (Table S1). Rarefaction plots suggest that, while most of the taxa present in these samples were found at these sequencing depths, undiscovered diversity may remain (Fig. S1). Only a single Crematogaster rochai sample was successfully sequenced so this species was excluded from all bacterial community comparisons. Demultiplexed sequence reads have been submitted to NCBI's Sequence Read Archive under accession number SRP033241. Alpha diversity Samples extracted using different methods were never significantly different in alpha diversity, the diversity within samples, whether species were analyzed together or individually (t-tests, P > 0.1). Results for all alpha diversity comparisons are shown in Table S3. Alpha diversity was significantly lower in both Pseudomyrmex species when compared to Cephalotes varians according to all three measures (richness, Shannon diversity, evenness; t-tests, P < 0.01), but alpha diversity did not differ significantly between Pseudomyrmex species. P. flavicornis was the only species for which both adult ant abdomens and whole larvae were successfully sequenced. Larvae had significantly higher alpha diversity within this species by all metrics (t-tests, P < 0.01). However, in both Pseudomyrmex species and Cephalotes varians, alpha diversity did not differ significantly between extractions of one and three individuals for adults or larvae (t-tests, P > 0.1), indicating that pooling at these scales is unlikely to artificially decrease recovered diversity, as has been previously reported (Manter et al. 2010). Alpha diversity was also significantly different between some colonies within Cephalotes varians and P. flavicornis. Cephalotes varians colony CSM1280 had a significantly lower alpha diversity than both CSM2194 and CSM1970 (t-tes