For this reason, culture-independent techniques, including single stranded confirmation polymorphisms (SSCP) analysis of DNA and restriction fragment length polymorphism (RFLP) typing of isolates, have been used increasingly to study the bacterial populations in milk and/or cheese [20]. Next Generation Sequencing (NGS) techniques are extremely useful because of the enhanced sequencing depth that can be achieved compared to previous technologies for relatively low cost without the bias introduced by culture techniques. To date, NGS methods have been applied most prolifically to describe the human microbiome [21], but they have also been widely used to describe a vast array of environmental

and agricultural ecologies, including microflora of trees [22] and tomato surfaces [23], and even GSK690693 for epidemiological approaches in hospital pathogen tracking [24]. This technology has also been used to study the bacterial diversity of other cheeses as well, including artisanal cheeses [25], traditional Polish cheeses [26], and Danish semi-hard cheese [27]. However, the application of NGS methods to evaluate food microbiomes is still in its infancy. Results We recovered 3708 high-quality 16SrRNA gene sequences with an average sequence length

of 370bp and 309 ± 92.6 (SD) sequences per enriched cheese sample. From the four replicate Brand C cheese samples, a total of 1284 ± 92.8 sequences were recovered, 1187 ± 137.55 sequences were recovered from Brand A cheese, and Brand B produced 1237 ± 59.1 sequences. To compare environments for differentially-abundant taxonomic groups at the 0.05 significance level, buy PF-6463922 Metastats (a program designed to identify significant taxonomic differences between microbial communities) [28] was used for phylum, class, order, family and genus level assignments. Average abundance of bacterial classifications are presented in Table 1 along with p-values of brand comparisons. Table 1 Average abundance (%) of sequences

GS-9973 assigned to taxa in all cheese brands   Classification Brand A (%) Brand B (%) Brand C (%) Significant Difference? (p ≤ 0.05) Phylum Firmicutes 68 100 81 (A and B, p = 0.006); A and C, p = 0.135; B and C, p = 0.0) Proteobacteria 29 0 19 (A and C, p = 0.141; A and B, p = 0.0; B and C, p = 0.012) Class Clostridia 66 0 0 (A and C, p = 0.004; A and B, p = 0.01) Gammaproteobacteria 22 0 19 (A and C, p = 0.65; A and B, p = 0.005; Nintedanib (BIBF 1120) B and C, p =0.0) Bacilli 2 100 81 (A and B, p = 0.0; A and C, p = 0.0; B and C, p = 0.011) Order Clostridiales 67 0 0 (A and C, p = 0.003; A and B, p = 0.004) Lactobacillales 0 0 22 (A and C, p = 0.005; C and B, p = 0.006) Enterobacteriales 9 0 14 (A and C, p = 0.03; A and B, p = 0.002; B and C, p = 0.012) Pseudomonadales 9 0 5 (A and C, p = 0.049; A and B, p = 0.049 B and C, p = 0.017) Bacillales 2 100 59 (A and B, p = 0.0; A and C, p = 0.0; B and C, p = 0.0) Family Incertae Sedis XII 0 96 45 (A and B, p = 0.0; A and C, p = 0.0; B and C, p = 0.0) Staphylococcaceae 0 3 0 (A and B, p = 0.