Genomic and phenotypic imprints of microbial domestication on cheese starter cultures
Bibliographic record
Abstract
Abstract Domestication – the artificial selection of wild species to obtain variants with traits of human interest– was integral to the rise of civilization. During the neolithic, the oversupply of food enabled by the domestication of crop plants and farm animals was associated with the invention of food preservation strategies through microbial fermentation. However, it remains unclear whether fermented food microbes show similar signs of domestication by humans like plants or animals. Only a few eukaroytic have been studied so far in this respect (e.g., yeasts used in mantou or wine), whereas little is known for bacteria. Here, we tested if cheese starter cultures harbour typical hallmarks of domestication by characterising over 100 community samples and over 100 individual strains isolated from historical and modern traditional Swiss cheese starter cultures. We find that cheese starter cultures have low genetic diversity both at the species and strain-level and are taxonomically and phenotypically stable. Our analyses further suggest that the evolutionary origin of the bacteria in cheese starter cultures coincided with the start of cheesemaking as reported from archeological records. Finally, we find evidence for ongoing genome decay and pseudogenization via transposon insertion related to a reduction of their niche breadth. These characteristics suggest that cheese starter cultures were domesticated by humans before knowing about microbes, potentially starting as early as the neolithics Future work documenting the prevalence of these hallmarks across diverse fermented food systems and geographic regions will be key to unveiling the joint history of humanity with fermented food microbes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".