Effect of Lineage-Specific Metabolic Traits of Lactobacillus reuteri on Sourdough Microbial Ecology
Bibliographic record
Abstract
This study determined the effects of specific metabolic traits of Lactobacillus reuteri on its competitiveness in sourdoughs. The competitiveness of lactobacilli in sourdough generally depends on their growth rate; acid resistance additionally contributes to competitiveness in sourdoughs with long fermentation times. Glycerol metabolism via glycerol dehydratase (gupCDE) accelerates growth by the regeneration of reduced cofactors; glutamate metabolism via glutamate decarboxylase (gadB) increases acid resistance by generating a proton motive force. Glycerol and glutamate metabolisms are lineage-specific traits in L. reuteri; therefore, this study employed glycerol dehydratase-positive sourdough isolates of human-adapted L. reuteri lineage I, glutamate decarboxylase-positive strains of rodent-adapted L. reuteri lineage II, as well as mutants with deletions in gadB or gupCDE. The competitivenesses of the strains were quantified by inoculation of wheat and sorghum sourdoughs with defined strains, followed by propagation of doughs with a 10% inoculum and 12-h or 72-h fermentation cycles. Lineage I L. reuteri strains dominated sourdoughs propagated with 12-h fermentation cycles; lineage II L. reuteri strains dominated sourdoughs propagated with 72-h fermentation cycles. L. reuteri 100-23ΔgadB was outcompeted by its wild-type strain in sourdoughs fermented with 72-h fermentation cycles; L. reuteri FUA3400ΔgupCDE was outcompeted by its wild-type strain in sourdoughs fermented with both 12-h and 72-h fermentation cycles. Competition experiments with isogenic pairs of strains resulted in a constant rate of strain displacement of the less competitive mutant strain. In conclusion, lineage-specific traits of L. reuteri determine the competitiveness of this species in sourdough fermentations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".