Mock Bacterial Community Research Articles

Bacterial mock communities as standards for reproducible cytometric microbiome analysis.

Flow cytometry has recently established itself as a tool to track short-term dynamics in microbial community assembly and link those dynamics with ecological parameters. However, instrumental configurations of commercial cytometers and variability introduced through differential handling of the cells and instruments frequently cause data set variability at the single-cell level. This is especially pronounced with microorganisms, which are in the lower range of optical resolution. Although alignment beads are valuable to generally minimize instrumental noise and align overall machine settings, an artificial microbial cytometric mock community (mCMC) is mandatory for validating lab workflows and enabling comparison of data between experiments, thus representing a necessary reference standard for the reproducible cytometric characterization of microbial communities, especially in long-term studies. In this study, the mock community consisted of two Gram-positive and two Gram-negative bacterial strains, which can be assembled with respective subsets of cells, including spores, in any selected ratio or concentration. The preparation of the four strains takes a maximum of 5 d, and the stains are storable with either PFA/ethanol fixation at -20 °C or drying at 4 °C for at least 6 months. Starting from this stock, an mCMC can be assembled within 1 h. Fluorescence staining methods are presented and representatively applied with two high-resolution cell sorters and three benchtop flow cytometers. Benchmarked data sets allow the use of bioinformatic evaluation procedures to decode community behavior or convey qualified cell sorting decisions for subsequent high-resolution sequencing or proteomic routines.

Optimizing 16S rRNA gene profile analysis from low biomass nasopharyngeal and induced sputum specimens

BackgroundCareful consideration of experimental artefacts is required in order to successfully apply high-throughput 16S ribosomal ribonucleic acid (rRNA) gene sequencing technology. Here we introduce experimental design, quality control and “denoising” approaches for sequencing low biomass specimens.ResultsWe found that bacterial biomass is a key driver of 16S rRNA gene sequencing profiles generated from bacterial mock communities and that the use of different deoxyribonucleic acid (DNA) extraction methods [DSP Virus/Pathogen Mini Kit® (Kit-QS) and ZymoBIOMICS DNA Miniprep Kit (Kit-ZB)] and storage buffers [PrimeStore® Molecular Transport medium (Primestore) and Skim-milk, Tryptone, Glucose and Glycerol (STGG)] further influence these profiles. Kit-QS better represented hard-to-lyse bacteria from bacterial mock communities compared to Kit-ZB. Primestore storage buffer yielded lower levels of background operational taxonomic units (OTUs) from low biomass bacterial mock community controls compared to STGG. In addition to bacterial mock community controls, we used technical repeats (nasopharyngeal and induced sputum processed in duplicate, triplicate or quadruplicate) to further evaluate the effect of specimen biomass and participant age at specimen collection on resultant sequencing profiles. We observed a positive correlation (r = 0.16) between specimen biomass and participant age at specimen collection: low biomass technical repeats (represented by < 500 16S rRNA gene copies/μl) were primarily collected at < 14 days of age. We found that low biomass technical repeats also produced higher alpha diversities (r = − 0.28); 16S rRNA gene profiles similar to no template controls (Primestore); and reduced sequencing reproducibility. Finally, we show that the use of statistical tools for in silico contaminant identification, as implemented through the decontam package in R, provides better representations of indigenous bacteria following decontamination.ConclusionsWe provide insight into experimental design, quality control steps and “denoising” approaches for 16S rRNA gene high-throughput sequencing of low biomass specimens. We highlight the need for careful assessment of DNA extraction methods and storage buffers; sequence quality and reproducibility; and in silico identification of contaminant profiles in order to avoid spurious results.

SMETASeq: Combined Profiling of Microbiota and Host Small RNAs.

SummaryUnderstanding microbial communities' roles in human health and disease requires methods that accurately characterize the microbial composition and their activity and effects within human biological samples. We present sMETASeq (small RNA Metagenomics by Sequencing), a novel method that uses sequencing of small RNAs to jointly measure host small RNA expression and create metagenomic profiles and detect small bacterial RNAs. We evaluated the performance of sMETASeq on a mock bacterial community and demonstrated its use on different human samples, including colon cancer, oral leukoplakia, cervix cancer, and a panel of human biofluids. In all datasets, the detected microbes reflected the biology of the different sample types.

SMETASeq: Combined Profiling of Microbiota and Host Small RNAs

Understanding microbial communities’ roles in human health and disease requires methods that accurately characterize the microbial composition and their activity and effects within human biological samples. We present sMETASeq (small RNA Metagenomics by Sequencing), a novel method that uses sequencing of small RNAs to jointly measure host small RNA expression and create metagenomic profiles. We evaluated the performance of sMETASeq on a mock bacterial community and demonstrated its use on different human samples, including colon cancer, oral leukoplakia, cervix cancer and a panel of human biofluids. In all datasets, the detected microbes reflected the biology of the different sample types.

Impact of Host DNA and Sequencing Depth on the Taxonomic Resolution of Whole Metagenome Sequencing for Microbiome Analysis.

The amount of host DNA poses a major challenge to metagenome analysis. However, there is no guidance on the levels of host DNA, nor on the depth of sequencing needed to acquire meaningful information from whole metagenome sequencing (WMS). Here, we evaluated the impact of a wide range of amounts of host DNA and sequencing depths on microbiome taxonomic profiling using WMS. Synthetic samples with increasing levels of host DNA were created by spiking DNA of a mock bacterial community, with DNA from a mouse-derived cell line. Taxonomic analysis revealed that increasing proportions of host DNA led to decreased sensitivity in detecting very low and low abundant species. Reduction of sequencing depth had major impact on the sensitivity of WMS for profiling samples with 90% host DNA, increasing the number of undetected species. Finally, analysis of simulated datasets with fixed depth of 10 million reads confirmed that microbiome profiling becomes more inaccurate as the level of host DNA increases in a sample. In conclusion, samples with high amounts of host DNA coupled with reduced sequencing depths, decrease WMS coverage for characterization of the microbiome. This study highlights the importance of carefully considering these aspects in the design of WMS experiments to maximize microbiome analyses.

A 16S rDNA PCR-based theoretical to actual delta approach on culturable mock communities revealed severe losses of diversity information

BackgroundSubunits of ribosomal RNA genes (rDNAs) characterized by PCR-based protocols have been the proxy for studies in microbial taxonomy, phylogenetics, evolution and ecology. However, relevant factors have shown to interfere in the experimental outputs in a variety of systems. In this work, a ‘theoretical’ to ‘actual’ delta approach was applied to data on culturable mock bacterial communities (MBCs) to study the levels of losses in operational taxonomic units (OTUs) detectability. Computational and lab-bench strategies based on 16S rDNA amplification by 799F and U1492R primers were employed, using a fingerprinting method with highly improved detectability of fragments as a case-study tool. MBCs were of two major types: in silico MBCs, assembled with database-retrieved sequences, and in vitro MBCs, with AluI digestions of PCR data generated from culturable endophytes isolated from cacao trees.ResultsInterfering factors for the 16 s rDNA amplifications, such as the type of template, direct and nested PCR, proportion of chloroplast DNA from a tropical plant source (Virola officinalis), and biased-amplification by the primers resulted in altered bacterial 16S rDNA amplification, both on MBCs and V. officinalis leaf-extracted DNA. For the theoretical data, the maximum number of fragments for in silico and in vitro cuts were not significantly different from each other. Primers’ preferences for certain sequences were detected, depending on the MBCs’ composition prior to PCR. The results indicated overall losses from 2.3 up to 8.2 times in the number of OTUs detected from actual AluI digestions of MBCs when compared to in silico and in vitro theoretical data.ConclusionsDue to all those effects, the final amplification profile of the bacterial community assembled was remarkably simplified when compared to the expected number of detectable fragments known to be present in the MBC. From these findings, the scope of hypotheses generation and conclusions from experiments based on PCR amplifications of bacterial communities was discussed.

Quantification of variation and the impact of biomass in targeted 16S rRNA gene sequencing studies

BackgroundRecent advances in sequencing technologies and bioinformatics tools have allowed for large-scale microbiome studies that are rapidly advancing medical research. However, small changes in technique or analysis can significantly alter the results and lead to conflicting findings. Quantifying the technical versus biological variation expected in targeted 16S rRNA gene sequencing studies and how this variation changes with input biomass is critical to guide meaningful interpretation of the current literature and plan future research.ResultsData were compiled from 469 sequencing libraries across 19 separate targeted 16S rRNA gene sequencing runs over a 2.5-year time period. Following removal of contaminant sequences identified from negative controls, 244 samples retained sufficient reads for further analysis. Coefficients of variation for intra- and inter-assay variation from repeated measurements of a bacterial mock community ranged from 8.7 to 37.6% (intra) and 15.6 to 80.5% (inter) for all but one genus of bacteria whose relative abundance was greater than 1%. Intra- versus inter-assay Bray-Curtis pairwise distances for a single stool sample were 0.11 versus 0.31, whereas intra-assay variation from repeat stool samples from the same donor was greater at 0.38 (Wilcoxon p = 0.001). A dilution series of the bacterial mock community was used to assess the effect of input biomass on variability. Pairwise distances increased with more dilute samples, and estimates of relative abundance became unreliable below approximately 100 copies of the 16S rRNA gene per microliter. Using this data, we created a prediction model to estimate the expected variation in microbiome measurements for given input biomass and relative abundance values.ConclusionsWell-controlled microbiome studies are sufficiently robust to capture small biological effects and can achieve levels of variability consistent with clinical assays. Relative abundance is negatively associated with measures of variability and has a stronger effect on variability than does absolute biomass, suggesting that it is feasible to detect differences in bacterial populations in very low-biomass samples. Further, by quantifying the effect of biomass and relative abundance on compositional variability, we developed a tool for defining the expected variance in a given microbiome study.

A High-Throughput Organoid Microinjection Platform to Study Gastrointestinal Microbiota and Luminal Physiology.

Background & AimsThe human gut microbiota is becoming increasingly recognized as a key factor in homeostasis and disease. The lack of physiologically relevant in vitro models to investigate host–microbe interactions is considered a substantial bottleneck for microbiota research. Organoids represent an attractive model system because they are derived from primary tissues and embody key properties of the native gut lumen; however, access to the organoid lumen for experimental perturbation is challenging. Here, we report the development and validation of a high-throughput organoid microinjection system for cargo delivery to the organoid lumen and high-content sampling.MethodsA microinjection platform was engineered using off-the-shelf and 3-dimensional printed components. Microinjection needles were modified for vertical trajectories and reproducible injection volumes. Computer vision (CVis) and microfabricated CellRaft Arrays (Cell Microsystems, Research Triangle Park, NC) were used to increase throughput and enable high-content sampling of mock bacterial communities. Modeling preformed using the COMSOL Multiphysics platform predicted a hypoxic luminal environment that was functionally validated by transplantation of fecal-derived microbial communities and monocultures of a nonsporulating anaerobe.ResultsCVis identified and logged locations of organoids suitable for injection. Reproducible loads of 0.2 nL could be microinjected into the organoid lumen at approximately 90 organoids/h. CVis analyzed and confirmed retention of injected cargos in approximately 500 organoids over 18 hours and showed the requirement to normalize for organoid growth for accurate assessment of barrier function. CVis analyzed growth dynamics of a mock community of green fluorescent protein– or Discosoma sp. red fluorescent protein-expressing bacteria, which grew within the organoid lumen even in the presence of antibiotics to control media contamination. Complex microbiota communities from fecal samples survived and grew in the colonoid lumen without appreciable changes in complexity.ConclusionsHigh-throughput microinjection into organoids represents a next-generation in vitro approach to investigate gastrointestinal luminal physiology and the gastrointestinal microbiota.

A portable system for rapid bacterial composition analysis using a nanopore-based sequencer and laptop computer

We developed a portable system for 16S rDNA analyses consisting of a nanopore technology-based sequencer, the MinION, and laptop computers, and assessed its potential ability to determine bacterial compositions rapidly. We tested our protocols using a mock bacterial community that contained equimolar 16S rDNA and a pleural effusion from a patient with empyema, for time effectiveness and accuracy. MinION sequencing targeting 16S rDNA detected all 20 of the bacterial species present in the mock bacterial community. Time course analysis indicated that the sequence data obtained during the first 5 minutes of sequencing (1,379 bacterial reads) were enough to detect all 20 bacteria in the mock sample and to determine species composition, consistent with results of those obtained from 4 hours of sequencing (24,202 reads). Additionally, using a clinical sample extracted from the empyema patient’s pleural effusion, we could identify major bacterial pathogens in that effusion using our rapid sequencing and analysis protocol. All results are comparable to conventional 16S rDNA sequencing results using an IonPGM sequencer. Our results suggest that rapid sequencing and bacterial composition determination are possible within 2 hours after obtaining a DNA sample.

MGmapper: Reference based mapping and taxonomy annotation of metagenomics sequence reads.

An increasing amount of species and gene identification studies rely on the use of next generation sequence analysis of either single isolate or metagenomics samples. Several methods are available to perform taxonomic annotations and a previous metagenomics benchmark study has shown that a vast number of false positive species annotations are a problem unless thresholds or post-processing are applied to differentiate between correct and false annotations. MGmapper is a package to process raw next generation sequence data and perform reference based sequence assignment, followed by a post-processing analysis to produce reliable taxonomy annotation at species and strain level resolution. An in-vitro bacterial mock community sample comprised of 8 genuses, 11 species and 12 strains was previously used to benchmark metagenomics classification methods. After applying a post-processing filter, we obtained 100% correct taxonomy assignments at species and genus level. A sensitivity and precision at 75% was obtained for strain level annotations. A comparison between MGmapper and Kraken at species level, shows MGmapper assigns taxonomy at species level using 84.8% of the sequence reads, compared to 70.5% for Kraken and both methods identified all species with no false positives. Extensive read count statistics are provided in plain text and excel sheets for both rejected and accepted taxonomy annotations. The use of custom databases is possible for the command-line version of MGmapper, and the complete pipeline is freely available as a bitbucked package (https://bitbucket.org/genomicepidemiology/mgmapper). A web-version (https://cge.cbs.dtu.dk/services/MGmapper) provides the basic functionality for analysis of small fastq datasets.

Does a prenatal bacterial microbiota exist?

Does a prenatal bacterial microbiota exist?

From reads to operational taxonomic units: an ensemble processing pipeline for MiSeq amplicon sequencing data.

The development of high-throughput sequencing technologies has provided microbial ecologists with an efficient approach to assess bacterial diversity at an unseen depth, particularly with the recent advances in the Illumina MiSeq sequencing platform. However, analyzing such high-throughput data is posing important computational challenges, requiring specialized bioinformatics solutions at different stages during the processing pipeline, such as assembly of paired-end reads, chimera removal, correction of sequencing errors, and clustering of those sequences into Operational Taxonomic Units (OTUs). Individual algorithms grappling with each of those challenges have been combined into various bioinformatics pipelines, such as mothur, QIIME, LotuS, and USEARCH. Using a set of well-described bacterial mock communities, state-of-the-art pipelines for Illumina MiSeq amplicon sequencing data are benchmarked at the level of the amount of sequences retained, computational cost, error rate, and quality of the OTUs. In addition, a new pipeline called OCToPUS is introduced, which is making an optimal combination of different algorithms. Huge variability is observed between the different pipelines in respect to the monitored performance parameters, where in general the amount of retained reads is found to be inversely proportional to the quality of the reads. By contrast, OCToPUS achieves the lowest error rate, minimum number of spurious OTUs, and the closest correspondence to the existing community, while retaining the uppermost amount of reads when compared to other pipelines. The newly introduced pipeline translates Illumina MiSeq amplicon sequencing data into high-quality and reliable OTUs, with improved performance and accuracy compared to the currently existing pipelines.

Accuracy of commercial kits and published primer pairs for the detection of periodontopathogens.

ObjectivesDespite the input of microbiome research, a group of 20 bacteria continues to be the focus of periodontal diagnostics and therapy. The aim of this study was to compare three commercial kits and laboratory-developed primer pairs for effectiveness in detecting such periodontopathogens.Materials and methodsFourteen bacterial mock communities, consisting of 16 randomly assembled bacterial strains, were used as reference standard for testing kits and primers. Extracted DNA from mock communities was analyzed by PCR in-house with specific primers and forwarded for analysis to the manufacturer’s laboratory of each of the following kits: ParoCheck®Kit 20, micro-IDent®plus11, and Carpegen® Perio Diagnostik.ResultsThe kits accurately detected Fusobacterium nucleatum, Prevotella intermedia/Prevotella nigrescens, Parvimonas micra, Aggregatibacter actinomycetemcomitans, Campylobacter rectus/showae, Streptococcus mitis, Streptococcus mutans, and Veillonella parvula. The in-house primers for F.nucleatum were highly specific to subtypes of the respective periopathogen. Other primers repeatedly detected oral pathogens not present in the mock communities, indicating reduced specificity.ConclusionsThe commercial kits used in this study are reliable tools to support periodontal diagnostics. Whereas the detection profile of the kits is fixed at a general specificity level, the design of primers can be adjusted to differentiate between highly specific strains. In-house primers are more error-prone. Bacterial mock communities can be established as a reference standard for any similar testing.Clinical relevanceThe tested kits render good results with selected bacterial species. Primers appear to be less useful for routine clinical diagnostics and of limited applicability in research. Basic information about the periodontopathogens identified in this study supports clinical decision-making.

Modular approach to customise sample preparation procedures for viral metagenomics: a reproducible protocol for virome analysis.

A major limitation for better understanding the role of the human gut virome in health and disease is the lack of validated methods that allow high throughput virome analysis. To overcome this, we evaluated the quantitative effect of homogenisation, centrifugation, filtration, chloroform treatment and random amplification on a mock-virome (containing nine highly diverse viruses) and a bacterial mock-community (containing four faecal bacterial species) using quantitative PCR and next-generation sequencing. This resulted in an optimised protocol that was able to recover all viruses present in the mock-virome and strongly alters the ratio of viral versus bacterial and 16S rRNA genetic material in favour of viruses (from 43.2% to 96.7% viral reads and from 47.6% to 0.19% bacterial reads). Furthermore, our study indicated that most of the currently used virome protocols, using small filter pores and/or stringent centrifugation conditions may have largely overlooked large viruses present in viromes. We propose NetoVIR (Novel enrichment technique of VIRomes), which allows for a fast, reproducible and high throughput sample preparation for viral metagenomics studies, introducing minimal bias. This procedure is optimised mainly for faecal samples, but with appropriate concentration steps can also be used for other sample types with lower initial viral loads.

Evaluation of shotgun metagenomics sequence classification methods using in silico and in vitro simulated communities.

BackgroundThe field of metagenomics (study of genetic material recovered directly from an environment) has grown rapidly, with many bioinformatics analysis methods being developed. To ensure appropriate use of such methods, robust comparative evaluation of their accuracy and features is needed. For taxonomic classification of sequence reads, such evaluation should include use of clade exclusion, which better evaluates a method’s accuracy when identical sequences are not present in any reference database, as is common in metagenomic analysis. To date, relatively small evaluations have been performed, with evaluation approaches like clade exclusion limited to assessment of new methods by the authors of the given method. What is needed is a rigorous, independent comparison between multiple major methods, using the same in silico and in vitro test datasets, with and without approaches like clade exclusion, to better characterize accuracy under different conditions.ResultsAn overview of the features of 38 bioinformatics methods is provided, evaluating accuracy with a focus on 11 programs that have reference databases that can be modified and therefore most robustly evaluated with clade exclusion. Taxonomic classification of sequence reads was evaluated using both in silico and in vitro mock bacterial communities. Clade exclusion was used at taxonomic levels from species to class—identifying how well methods perform in progressively more difficult scenarios. A wide range of variability was found in the sensitivity, precision, overall accuracy, and computational demand for the programs evaluated. In experiments where distilled water was spiked with only 11 bacterial species, frequently dozens to hundreds of species were falsely predicted by the most popular programs. The different features of each method (forces predictions or not, etc.) are summarized, and additional analysis considerations discussed.ConclusionsThe accuracy of shotgun metagenomics classification methods varies widely. No one program clearly outperformed others in all evaluation scenarios; rather, the results illustrate the strengths of different methods for different purposes. Researchers must appreciate method differences, choosing the program best suited for their particular analysis to avoid very misleading results. Use of standardized datasets for method comparisons is encouraged, as is use of mock microbial community controls suitable for a particular metagenomic analysis.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0788-5) contains supplementary material, which is available to authorized users.

The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies.

BackgroundCharacterizing microbial communities via next-generation sequencing is subject to a number of pitfalls involving sample processing. The observed community composition can be a severe distortion of the quantities of bacteria actually present in the microbiome, hampering analysis and threatening the validity of conclusions from metagenomic studies. We introduce an experimental protocol using mock communities for quantifying and characterizing bias introduced in the sample processing pipeline. We used 80 bacterial mock communities comprised of prescribed proportions of cells from seven vaginally-relevant bacterial strains to assess the bias introduced in the sample processing pipeline. We created two additional sets of 80 mock communities by mixing prescribed quantities of DNA and PCR product to quantify the relative contribution to bias of (1) DNA extraction, (2) PCR amplification, and (3) sequencing and taxonomic classification for particular choices of protocols for each step. We developed models to predict the “true” composition of environmental samples based on the observed proportions, and applied them to a set of clinical vaginal samples from a single subject during four visits.ResultsWe observed that using different DNA extraction kits can produce dramatically different results but bias is introduced regardless of the choice of kit. We observed error rates from bias of over 85% in some samples, while technical variation was very low at less than 5% for most bacteria. The effects of DNA extraction and PCR amplification for our protocols were much larger than those due to sequencing and classification. The processing steps affected different bacteria in different ways, resulting in amplified and suppressed observed proportions of a community. When predictive models were applied to clinical samples from a subject, the predicted microbiome profiles were better reflections of the physiology and diagnosis of the subject at the visits than the observed community compositions.ConclusionsBias in 16S studies due to DNA extraction and PCR amplification will continue to require attention despite further advances in sequencing technology. Analysis of mock communities can help assess bias and facilitate the interpretation of results from environmental samples.Electronic supplementary materialThe online version of this article (doi:10.1186/s12866-015-0351-6) contains supplementary material, which is available to authorized users.

The skin microbiome in psoriatic arthritis: methodology development and pilot data

BackgroundSkin microbiota are likely to be important in the development of conditions such as psoriatic arthritis. Profiling the bacterial community in the psosriatic plaques will contribute to our understanding of the role of the skin microbiome in these conditions. The aim of this work was to determine the optimum study design for work on the skin microbiome with use of the MiSeq platform. The objectives were to compare data generated from two platforms for two primer pairs in a low density mock bacterial community. MethodsDNA was obtained from two low density mock communities of 11 diverse bacterial strains (with and without human DNA supplementation) and from swabs taken from the skin of four healthy volunteers. The DNA was amplified with primer pairs covering hypervariable regions of the 16S rRNA gene: primers 63F and 519R (V1-V3), and 347F and 803R (V3-V4). The resultant libraries were indexed for the MiSeq and Roche454 platforms and sequenced. Both datasets were de-noised, cleaned of chimeras, and analysed by use of QIIME software (version 1.8.0). FindingsNo significant difference in the diversity indices at the phylum and the genus level between the platforms was seen. Comparison of the diversity indices for the mock community data for the two primer pairs demonstrated that the V3-V4 hypervariable region had significantly better capture of bacterial diversity than did the V1-V3 region. Amplification with the same primer pairs showed strong concordance within each platform (98·9–99·8%), with negligible effect of spiked human DNA contamination. Comparison at the family level classification between samples processed on the MiSeq and Roche454 platforms using the V3-V4 hypervariable region also showed a high level of concordance (87%), although less so for the V1-V3 primers (10%). The pilot data from healthy volunteers were similar. InterpretationResults obtained from the V3-V4 16S rRNA hypervariable region, sequencing on the MiSeq and Roche454 platforms, were concordant between replicates, and between each other. These findings suggest that the MiSeq platform, and these primers, is a comparable method for determining skin microbiota to the widely used Roche454 methodology. FundingNIHR Manchester Musculoskeletal Biomedical Research Unit.

16S rRNA gene pyrosequencing of reference and clinical samples and investigation of the temperature stability of microbiome profiles.

BackgroundSample storage conditions, extraction methods, PCR primers, and parameters are major factors that affect metagenomics analysis based on microbial 16S rRNA gene sequencing. Most published studies were limited to the comparison of only one or two types of these factors. Systematic multi-factor explorations are needed to evaluate the conditions that may impact validity of a microbiome analysis. This study was aimed to improve methodological options to facilitate the best technical approaches in the design of a microbiome study. Three readily available mock bacterial community materials and two commercial extraction techniques, Qiagen DNeasy and MO BIO PowerSoil DNA purification methods, were used to assess procedures for 16S ribosomal DNA amplification and pyrosequencing-based analysis. Primers were chosen for 16S rDNA quantitative PCR and amplification of region V3 to V1. Swabs spiked with mock bacterial community cells and clinical oropharyngeal swabs were incubated at respective temperatures of -80°C, -20°C, 4°C, and 37°C for 4 weeks, then extracted with the two methods, and subjected to pyrosequencing and taxonomic and statistical analyses to investigate microbiome profile stability.ResultsThe bacterial compositions for the mock community DNA samples determined in this study were consistent with the projected levels and agreed with the literature. The quantitation accuracy of abundances for several genera was improved with changes made to the standard Human Microbiome Project (HMP) procedure. The data for the samples purified with DNeasy and PowerSoil methods were statistically distinct; however, both results were reproducible and in good agreement with each other. The temperature effect on storage stability was investigated by using mock community cells and showed that the microbial community profiles were altered with the increase in incubation temperature. However, this phenomenon was not detected when clinical oropharyngeal swabs were used in the experiment.ConclusionsMock community materials originated from the HMP study are valuable controls in developing 16S metagenomics analysis procedures. Long-term exposure to a high temperature may introduce variation into analysis for oropharyngeal swabs, suggestive of storage at 4°C or lower. The observed variations due to sample storage temperature are in a similar range as the intrapersonal variability among different clinical oropharyngeal swab samples.