Brevibacterium from Austrian hard cheese harbor a putative histamine catabolism pathway and a plasmid for adaptation to the cheese environment

Abstract

The genus Brevibacterium harbors many members important for cheese ripening. We performed real-time quantitative PCR (qPCR) to determine the abundance of Brevibacterium on rinds of Vorarlberger Bergkäse, an Austrian artisanal washed-rind hard cheese, over 160 days of ripening. Our results show that Brevibacterium are abundant on Vorarlberger Bergkäse rinds throughout the ripening time. To elucidate the impact of Brevibacterium on cheese production, we analysed the genomes of three cheese rind isolates, L261, S111, and S22. L261 belongs to Brevibacterium aurantiacum, whereas S111 and S22 represent novel species within the genus Brevibacterium based on 16S rRNA gene similarity and average nucleotide identity. Our comparative genomic analysis showed that important cheese ripening enzymes are conserved among the genus Brevibacterium. Strain S22 harbors a 22 kb circular plasmid which encodes putative iron and hydroxymethylpyrimidine/thiamine transporters. Histamine formation in fermented foods can cause histamine intoxication. We revealed the presence of a putative metabolic pathway for histamine degradation. Growth experiments showed that the three Brevibacterium strains can utilize histamine as the sole carbon source. The capability to utilize histamine, possibly encoded by the putative histamine degradation pathway, highlights the importance of Brevibacterium as key cheese ripening cultures beyond their contribution to cheese flavor production.

Figure 1

Abundance of Brevibacterium on cheese rinds during ripening of Vorarlberger Bergkäse in two different cheese production plants determined by qPCR. Bacterial cell equivalents (BCE) per 0.5 g cheese rind during ripening in two different cheese production facilities are shown. Graphs show median and interquartile ranges for the 20 samples from each plant [(A): plant A, and (B): plant B] for each analyzed day of ripening (0, 14, 30, 90 and 160 days). Statistically significant differences of Brevibacterium BCEs are highlighted by asterisks, with *indicating p < 0.05 and **indicating p < 0.01. Numerical BCE values are shown in Table S1, p-values are shown in Table S2.

Figure 2

Phylogenetic relationships of Brevibacterium strains based on 16S rRNA gene sequences. The evolutionary history was inferred by using the Maximum Likelihood method based on the Tamura-Nei model. The tree with the highest log likelihood (−4232.79) is shown. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 35 nucleotide sequences. All positions containing gaps and missing data were eliminated. There were a total of 1127 positions in the final dataset. Evolutionary analyses were conducted in MEGA7. Isolates obtained in this study are highlighted in bold. Type strains are indicated by “(T)”, GenBank accession numbers are shown in parentheses. Black dots indicate Maximum Likelihood, Neighbor-Joining and Maximum Parsimony bootstrap values higher than 85 (1000x resampling).

Figure 3

Growth of Brevibacterium strains S22, S111, and L261 in minimal medium (MM) with or without 10 mM histamine as sole carbon source. Growth was determined using optical density measurements at 600 nm (OD₆₀₀). Values represent mean values ± SEM.