Fig 1.
Alpha-diversity measure using QIIME.
Plots shown represent estimated OTU abundance in increasingly rarefied subsamples (n = 50) of the original data set. Error bars represent standard deviation. Alpha-rarefaction plots were generated in QIIME using the observed_species metric for estimating alpha (within-sample) diversity.
Fig 2.
3-Dimensional Weighted Unifrac Principal Component Analysis showing replicate, sample and type variability.
Legend: 1: D1 2: D2 3: D3 4: D4 5: D5 6: D6 7: M1 8: M2 9: M3 10: M4 11: M5 12: M6 13: M7 14: TB1 15: TB2. Weighted UniFrac distances were used to ordinate the samples, allowing visualization of within and between replicate variability.
Table 1.
Statistical Analysis of within and between-sample differences based on Analysis of Variances (ANOVA).
Fig 3.
Relative abundance at family level of taxonomy.
The relative abundances at the family level of taxonomic classification were calculated using QIIME. Each bacterial family is represented as a different color in the bar graphs below. Combined relative abundances total 100% for each individual product. Numbering is as follows: 1) Brevibacteriaceae 2) Corynebacteriaceae 3) Dermabacteraceae 4) Microbacteriaceae 5) Micrococcaceae 6) Promicromonosporaceae 7) Yaniellaceae 8) Sphingobacteriaceae 9) Bacillaceae 10) Planococcaceae 11) Staphylococcaceae 12) Aerococcaceae 13) Carnobacteriaceae 14) Enterococcaceae 15) Lactobacillaceae 16) Leuconostocaceae 17) Acetobacteraceae 18) Alcaligenaceae 19) Comamonadaceae 20) Oxalobacteraceae 21) Enterobacteriaceae 22) Moraxellaceae 23) Pseudomonadaceae 24) Xanthomonadaceae 25) All Others (Bogoriellaceae, Flavobacteriaceae, Nocardiaceae, Aurantimonadaceae, Methylobacteriaceae, Rhizobiaceae, Alteromonadaceae, Halomonadaceae, and Rhodobacteraceae).
Table 2.
Predictive Logistical Discrimination of OTU/taxonomy vs Product Type.
Fig 4.
Heatmap displaying key nitrogen metabolism and other genes of interest in smokeless tobacco products.
Phylogenetic Investigations of Communities by Reconstruction of Unobserved States (PICRUSt) was used to obtain imputed metagenomic data based on 16S abundances. Displayed here are the predicted relative abundances of genes (grouped by KO terms) per 100,000 reads for (A) genes of interest including genes encoding toxins, antibiotic resistance, and pro-inflammatory molecules and (B) nitrogen metabolism pathway genes. Respiratory or dissimilatory nitrate reductases (vs. assimilatory) appear to be playing a large role in reduction of nitrate.
Fig 5.
Relative Percentage Contributions for Bacterial Families in Imputed Metagenome for toxin, antibiotic resistance, and pro-inflammatory molecule marker genes.
Heatmap representing the percent contributions by bacterial family for [A] K11041 exfoliative toxin A/B, [B] K07552 DHA1 family bicyclomycin/chloramphenicol resistance, [C] K02406 flagellin, [D] K02517 Lipid A biosynthesis, and [E] K11693 peptidoglycan biosynthesis imputed gene abundances. Only contributions above 0.5% are displayed in these heat maps.
Table 3.
Key nitrogen metabolism enzymes, TC/EC Number, corresponding microbial genes and their KEGG Orthology numbers.
Fig 6.
Relative Percentage Contributions for Bacterial Families in Imputed Metagenome for nitrogen metabolism genes.
Represented in this heatmap are percentage contributions by bacterial family for [A] respiratory nitrate reductase (narGHJI), [B] periplasmic nitrate reductase (napAB) and [C] assimitory nitrate reductase (nasAB) imputed gene abundances.