Comparative genomic and plasmid analysis of beer-spoiling and non-beer-spoiling <i>Lactobacillus brevis</i> isolates
Bibliographic record
Abstract
Beer-spoilage-related lactic acid bacteria (BSR LAB) belong to multiple genera and species; however, beer-spoilage capacity is isolate-specific and partially acquired via horizontal gene transfer within the brewing environment. Thus, the extent to which genus-, species-, or environment- (i.e., brewery-) level genetic variability influences beer-spoilage phenotype is unknown. Publicly available Lactobacillus brevis genomes were analyzed via BlAst Diagnostic Gene findEr (BADGE) for BSR genes and assessed for pangenomic relationships. Also analyzed were functional coding capacities of plasmids of LAB inhabiting extreme niche environments. Considerable genetic variation was observed in L. brevis isolated from clinical samples, whereas 16 candidate genes distinguish BSR and non-BSR L. brevis genomes. These genes are related to nutrient scavenging of gluconate or pentoses, mannose, and metabolism of pectin. BSR L. brevis isolates also have higher average nucleotide identity and stronger pangenome association with one another, though isolation source (i.e., specific brewery) also appears to influence the plasmid coding capacity of BSR LAB. Finally, it is shown that niche-specific adaptation and phenotype are plasmid-encoded for both BSR and non-BSR LAB. The ultimate combination of plasmid-encoded genes dictates the ability of L. brevis to survive in the most extreme beer environment, namely, gassed (i.e., pressurized) beer.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".