Mobilome of Brevibacterium aurantiacum Sheds Light on Its Genetic Diversity and Its Adaptation to Smear-Ripened Cheeses
Bibliographic record
Abstract
Brevibacterium aurantiacum is an actinobacterium that confers key organoleptic properties to washed-rind cheeses during the ripening process. Although this industrially relevant species has been gaining an increasing attention in the past years, its genome plasticity is still understudied due to the unavailability of complete genomic sequences. To add insights on the mobilome of this group, we sequenced the complete genomes of five dairy Brevibacterium strains and one non-dairy strain using PacBio RSII. We performed phylogenetic and pan-genome analyses, including comparisons with other publicly available Brevibacterium genomic sequences. Our phylogenetic analysis revealed that these five dairy strains, previously identified as B. linens, belong instead to the B. aurantiacum species. A high number of transposases and integrases were observed in the Brevibacterium spp. strains. In addition, we identified 14 and 12 new insertion sequences in B. aurantiacum and B. linens genomes, respectively. Several stretches of homologous DNA sequences were also found between B. aurantiacum and other cheese rind actinobacteria, suggesting horizontal gene transfer (HGT). A HGT region from an iRon Uptake/Siderophore Transport Island (RUSTI) and an iron uptake composite transposon were found in five B. aurantiacum genomes. These findings suggest that low iron availability in milk is a driving force in the adaptation of this bacterial species to this niche. Moreover, the exchange of iron uptake systems suggests cooperative evolution between cheese rind actinobacteria. We also demonstrated that the integrative and conjugative element BreLI (Brevibacterium Lanthipeptide Island) can excise from B. aurantiacum SMQ-1417 chromosome. Our comparative genomic analysis suggests that mobile genetic elements played an important role in the adaptation of B. aurantiacum to cheese ecosystems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".