High-throughput ecological interaction mapping of dairy microorganisms
Bibliographic record
Abstract
To engineer efficient microbial management strategies in the food industry, a comprehensive understanding of microbial interactions is crucial. Microorganisms live in communities where they influence each other in several ways. Although much attention has been paid to the production of antagonistic metabolites in lactic acid bacteria (LAB), research that accounts for the complexity of their ecological interactions and their dynamics remains limited. This study explores binary interactions within a mock community of 94 strains, including 23 LAB from culture collections and 71 isolated from dairy products. Using a colony-picking robot and image analysis, bidirectional interactions were measured at high throughput on solid media, where one test strain was challenged against other mock community members as the target strains. Assays of 15 test strains (14 LAB and one yeast) yielded 1,142 bidirectionally mapped interactions, classified by ecological type over seven days. The results showed variation in the strength, directionality, and type of interactions depending on the origin of the target strains. Cooperation rates increased for strains isolated from raw milk to pasteurized milk and cheese, while exploitation was more common in raw milk strains. Cooperating strains also exhibited more similar ecological behaviors than competing strains, suggesting the presence of microbial cliques. Interestingly, Lactococcus cremoris ATCC 19257 developed pink-red pigmentation when co-cultured with Pseudomonas aeruginosa. Overall, these findings present an unprecedented exploratory data set that significantly improves our understanding of microbial interactions at the system level, with potential applications in strain selection for food processes such as fermentation, bioprotection, and probiotics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".