Taxonomy, Sequence Variance and Functional Profiling of the Microbial Community of Long-Ripened Cheddar Cheese Using Shotgun Metagenomics
Bibliographic record
Abstract
Shotgun metagenomic sequencing was used to investigate the diversity of the microbial community of Cheddar cheese ripened over 32 months. The changes in taxa abundance were compared from assembly-based, non-assembly-based, and mOTUs2 sequencing pipelines to delineate the community profile for each age group. Metagenomic assembled genomes (MAGs) passing the quality threshold were obtained for 11 species from 58 samples. Although Lactococcus cremoris and Lacticaseibacillus paracasei were dominant across the shotgun samples, other species were identified using MG-RAST. NMDS analysis of the beta diversity of the microbial community revealed the similarity of the cheeses in older age groups (7 months to 32 months). As expected, the abundance of Lactococcus cremoris consistently decreased over ripening, while the proportion of permeable cells increased. Over the ripening period, the relative abundance of viable Lacticaseibacillus paracasei progressively increased, but at a variable rate among trials. Reads attributed to Siphoviridae and Ascomycota remained below 1% relative abundance. The functional profiles of PMA-treated cheeses differed from those of non-PMA-treated cheeses. Starter rotation was reflected in the single nucleotide variant profiles of Lactococcus cremoris (SNVs of this species using mOTUs2), while the incoming milk was the leading factor in discriminating Lacticaseibacillus paracasei/casei SNV profiles. The relative abundance estimates from Kraken2, non-assembly-based (MG-RAST) and marker gene clusters (mOTUs2) were consistent across age groups for the two dominant taxa. Metagenomics enabled sequence variant analysis below the bacterial species level and functional profiling that may affect the metabolic interactions between subpopulations in cheese during ripening, which could help explain the overall flavour development of cheese. Future work will integrate microbial variants with volatile profiles to associate the development of compounds related to cheese flavour at each ripening stage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".