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We demonstrate our results from analyzing a mock community of bacterial DNA where we determine how each hypervariable region differs in its utility to provide information on taxonomic classifications, alpha diversity, and beta diversity. We report that certain taxa are only identified by particular hypervariable regions, corroborating prior studies Claesson et al.
Furthermore, we discuss different options for downstream analysis and statistics, and demonstrate that using a generalized linear model GLM to statistically combine results from multiple hypervariable regions increases sensitivity of taxonomic classification.
Finally, we demonstrate the utility of our approach in the analysis of clinical samples in an illustrative clinical cohort. The strain composition of the mock community is given in Table 1. The mock community was sequenced a total of five times from four library preparations and over three sequencing runs.
A total of three 3 adult males self-collected two 2 rectal swab samples each with sterile flocked swabs Cat. One rectal swab from each individual was randomly selected for DNA extraction immediately after sample collection RS1. The DNA extraction protocol was adapted from our previously published protocol Shrestha et al.
L, Sigma-Aldrich, St. M, Sigma-Aldrich, St. Louis, MO and phenol:chloroform:isoamyl alcohol , Cat. P, Molzym, Bremen, Germany. Libraries were then diluted down to 26 pM and pooled, yielding a 26 pM solution. Samples in this study were sequenced across three separate sequencing runs on Ion and Ion chips using bp sequencing kits.
Sequences were demultiplexed by sample using the S5 device software, and then separated per hypervariable region by ThermoFisher prior to downstream analysis. Manifest files were then created for each hypervariable region and each sequencing run. QIIME2 v DADA2 was used to denoise data, using the denoise-pyro plugin and parameters of 0 bp for trimming and truncation Callahan et al.
A separate DADA2 run was performed for each hypervariable region and each sequencing run. Denoising statistics were then summarized and exported to Psummarize-qc and Psummarize-qc directories in the analysis folder of the it-workflow repository for the ATCC mock community samples and the clinical samples, respectively. From these summaries, we determined that all samples in all hypervariable regions had a minimum of 10, reads which passed the filter in the DADA2 step.
Thus, we decided that 10, reads was an acceptable sampling depth. DADA2 feature tables and representative sequence files were then merged across sequencing runs so that there was only one feature table and representative sequence file per hypervariable region. Community diversity was analyzed using the core-metrics-phylogenetic plugin. Briefly, the feature table produced by open-reference OTU clustering and the phylogenetic trees constructed in the previous step were input into the core-metrics-phylogenetic plugin, which performed alpha and beta diversity analyses at a sampling depth of 10, reads.
Data was imported into Rstudio for visualization of alpha diversity metrics and principal coordinates analysis PCoA. Taxonomic classification results from each hypervariable region were aggregated into summary tables at higher taxonomic levels phylum through species for downstream comparative analysis. Beta-diversity distance matrices using the measures bray-curtis, jaccard, unweighted-unifrac, and weighted-unifrac were based on OTU profiles and were generated for each hypervariable region separately to account for region-specific OTUs.
Additionally, a multi-region beta-diversity analysis incorporated species level assignments across all hypervariable regions, followed by distance matrix calculation Canberra, Bray-Curtis, Jaccard, Euclidean, Gower, and Kulczynski using the vegdist command in the vegan R package.
It is well known that curating existing taxonomic databases can lead to improved performance Ritari et al. This refined database sfanos-db Prior to using sfanos-db First, we separated the sequences in the culture collection by hypervariable region to mimic our own data. This method allowed us to break down the culture collection sequences into their different hypervariable regions and simulate more complex clinical data.
Results were categorized into true positives TP , false positives FP , and false negatives FN based on whether they were found in the culture collection or not Supplementary File S1. Sequence assignment counts were converted to percent by adding up the total number of sequences that were assigned as TP, FP, or FN for each V region, dividing by the total number of sequences for that region, and multiplying by The output. This file and the feature-table.
Heatmaps were created in R using the pheatmap package and taxa-normalize-pct-per-region. Contaminant sequences were filtered out from the ATCC sample data. Any taxa that were detected in only one of the five technical replicates, detected at less than 0. Contaminants are listed in Supplementary Table S1. We used the generalized linear model function in Base R to evaluate statistical differences in alpha diversity and individual taxonomic abundance between fresh versus frozen samples in the clinical cohort.
Input feature values were log transformed in order to increase stability of values from person to person when performing statistics. In order to test our analysis pipeline Figure 1 we prepared libraries and sequenced DNA from a mock microbial community Table 1. A total of five independent replicates from four library preparations of the mock community were sequenced over three sequencing runs. We filtered out low-level contaminants Supplementary Table S1 prior to performing community alpha and beta diversity and taxonomic abundance analyses see Methods.
Schematic diagram of workflow. The four major steps in our workflow include 1 sample handling, from sample collection to sequencing 2 pre-processing of sequencing data and taxonomic reference database 3 performing microbiome bioinformatics using QIIME2 and 4 statistical analysis of results using R. V9 had significantly decreased alpha diversity compared to all regions across all metrics Supplementary File S2.
Alpha diversity analyses of mock community technical replicates by hypervariable region. Statistical analysis and p values can be found in Supplementary File S2. To compare beta diversity between hypervariable regions and circumvent the issue that OTUs would be region-specific, we used taxonomic results from each hypervariable region to create aggregated distance matrices.
In the plot based on the Canberra distance matrix Figure 3F , the V2, V3, V4, and V hypervariable regions clustered together, whereas V8 and V9 were distantly separated. This pattern was also observed by the other beta diversity metrics, with V sometimes also segregating slightly from V2, V3, and V4 which were largely clustered together. Principal coordinates analysis of mock community samples.
In addition to biodiversity measurements and beta diversity metrics, the percent abundance of the identified organisms after taxonomic classification was evaluated and is given in Supplementary File S3. The majority of species were identified by taxonomic classification of the sequences covering each hypervariable region, with the exception of V9 that only positively identified Escherichia coli and Acinetobacter baumannii.
Clostridium beijerinckii was the most difficult organism to speciate and was only correctly classified in V amplicons. The results with hypervariable regions V2, V3, and V4 only identified Clostridium beijerinckii at the genus level, V8 mis-classified it as Clostridium butyricum , and V9 did not identify any Clostridial organisms Supplementary File S3.
Aside from C. We next compared observed versus expected percent abundance by hypervariable region. There are copies of the 16 S rRNA gene in the bacterial genomes comprising the mock community. Taxonomic bar plots demonstrate the percent abundance of each taxon by hypervariable region compared to expected Figure 4.
Bolded taxa are those present in the 20 strain mix. Enterobacteriaceae unassigned 1 , Bifidobacterium unassigned 2 , Staphylococcus unassigned 3 , Enterococcus unassigned 4 , Clostridium sensu stricto 1 unassigned 5 , Enterobacter unassigned 6. Lastly, we performed a clustered heatmap analysis at the species level.
The resulting heatmap demonstrated that technical replicates of the mock community sequences cluster by hypervariable region Figure 5. The heatmap visually emphasizes the difference in taxonomic identification in V8 and particularly V9 compared to the other regions. It also highlights misclassifications and which regions were only able to classify taxa to the genus level.
Interestingly, the heatmap highlights a few misclassifications or false negatives that occurred in only a subset of the replicates. For example, Staphylococcus aureus was classified as Staphyloccoccus unassigned in replicates four and five. The OTU tables for these samples indicate that the sequence was truncated prematurely in replicates four and five, indicating the differences in classification here arise from library preparation or sequencing errors rather than downstream data analysis.
Bifidobacterium unassigned 1 , Enterobacteriaceae unassigned 2 , Staphylococcus unassigned 3 , Enterococcus unassigned 4 , Lactobacillus unassigned 5 , Enterobacter unassigned 6 , Clostridium sensu stricto 1 unassigned 7 , Unassigned at every taxonomic level 8. Since there appeared to be differing abilities of classification of bacterial species by hypervariable region in our ATCC data set, we next determined if this was the case for a larger pool of bacteria.
We plotted out the taxonomic classification results from our in silico database validation to visualize whether sensitivity and specificity was region specific Supplementary Figure S1. The sensitivity and mis-classification rates varied with respect to particular species and hypervariable regions. Alternatively, Bifidobacterium bifidum has high specificity across all hypervariable regions, implying that sensitivity and specificity of taxonomic classification may be increased by using data from multiple hypervariable regions.
We next sequenced and analyzed a set of six patient samples in order to demonstrate the use of a generalized linear model GLM in an illustrative clinical sample set, incorporating information from multiple hypervariable regions. Hypervariable regions V2, V3, V4, and V were included in the GLM, while data from the V8 and V9 regions were excluded due to their demonstrated poor performance in identifying species in the mock community Figures 2 — 5.
Samples consisted of duplicate rectal swabs from three participants. Libraries were prepared in tandem, and all samples were sequenced on the same sequencing run. Sequencing results were processed as outlined above Figure 1. Alpha diversity analyses of six clinical samples by type fresh or frozen and hypervariable region. Each patient provided two swabs, one of which was frozen prior to DNA extraction. We aggregated taxonomic results and used them to create Bray-Curtis, Jaccard, Canberra, Euclidean, Gower, and Kulczynski distance matrices in order to perform combined beta diversity analysis across all hypervariable regions.
As demonstrated by the Canberra PCoA plot in Figure 7 , most variation in beta diversity was due to different individuals and V9 sequences. PERMANOVA analysis of results from each individual hypervariable region demonstrated that total composition does not differ by fresh versus frozen status after adjusting for individual person and region-to-region variation Supplementary File S4.
Principal coordinates analysis of clinical cohort using Canberra distance matrix. Samples and regions from the same person are circled, excluding V9. Results cluster by individual and by V9 region not circled , but not by fresh versus frozen status. We next show that using a GLM that incorporates information from multiple variable regions increases the ability to detect significant differences between groups. This is demonstrated in Figure 8 , where we plot the average p-value for each specific taxon across all hypervariable regions against the p-value obtained for the same taxon when using a GLM.
Due to small sample size, we opted to use unadjusted p-values. There is an enrichment of significant p-values when using the GLM as seen by the shift upwards above the dashed line, indicating an increase in sensitivity compared to analyzing individual hypervariable regions. Using a GLM shows enrichment of taxonomic classification sensitivity. GLM p-values for specific taxa are plotted on the y -axis, and the mean p-value across all hypervariable regions for the same taxa are plotted on the x -axis.
The dashed line indicates where the p-values resulting from the GLM and from individual regions are equal. Enrichment above the dashed line indicates the GLM approach is more sensitive compared to analyzing individual regions. Using our GLM, we systematically compared abundance of taxa between fresh and frozen samples at multiple levels phylum, class, order, family, genus, species.
As an example, we chose to examine levels of Firmicutes , Bacteroidetes , and Faecalibacterium due to previous reports of differential abundance in fresh verses frozen samples Bahl et al. Our results showed no significant differences between these taxa Figure 9 or Firmicutes to Bacteroidetes ratios Figure While no concrete conclusions can be made from this data due to small sample size, we demonstrate the utility of the GLM using clinical samples.
Percent abundance of Bacteroidetes , Firmicutes , and Faecalibacterium by sample type fresh vs frozen and hypervariable region. PCR amplification using primers that target conserved regions of the 16 S rRNA gene and amplify across hypervariable regions allows amplification of DNA across a widespread taxonomic spectrum and provides unique sequences that can be used for taxonomic classification at higher levels e.
Next generation sequencing strategies are often limited to sequencing across only one or at most two of the nine hypervariable regions. Using a cohort of clinical samples, we demonstrate that taxonomic classification is enhanced by using a generalized linear multivariate model GLM that incorporates sequencing data from multiple hypervariable regions. Even with our limited mock community dataset, we observed hypervariable region-based differences in alpha diversity.
Most notably, taxa identified with V9 primers had significantly decreased alpha diversity compared to all other regions across all metrics. We performed six different beta diversity metrics Bray-Curtis, Jaccard, Canberra, Euclidean, Gower, and Kulczynski to evaluate differences between hypervariable regions.
Distance matrices used in beta diversity analyses are generated from OTU tables, however the OTUs identified were not consistent among hypervariable regions. Therefore, in order to compare results between hypervariable regions, we assembled distance matrices using taxonomic results. Hypervariable regions V8 and V9 clustered separately from the other regions, again demonstrating the poor performance of amplicon sequencing of these regions in assessing the constituents of the mock community sample.
Consistent with previous reports Claesson et al. Generally, those regions which identified more species present in the mock community also had more evenly distributed observed taxa i. Errors and biases that contribute to artifacts in PCR-based microbiome studies include sequence artifacts formation of chimeras or heteroduplexes, or polymerase errors , PCR bias differing amplification efficiencies of different templates , or biases in the analysis pipeline poorly discriminatory sequences Acinas et al.
Therefore, we deduce that the lack of diversity in the region is likely most related to PCR bias. Since V9 lacks sensitivity for many species, we opted to leave this region out of the generalized linear model we used on the clinical samples. V8 also tended to be less sensitive compared to V2, V3, V4, and V, and contributed to variation in the data according to PCoA plots. Therefore, V8 was excluded from further analyses as well. Notably, primer sequences for this kit are not available, and having access to primer sequences in this instance would aid in delving further into why V8 and V9 provided so little information.
For others attempting to incorporate a GLM into their analysis, we would recommend against using data from V8 and V9. One must also take into account whether specific regions have increased or decreased sensitivity for specific taxa of interest when considering which regions to include in your GLM.
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