Amino acids from root exudates induce Bacillus spore germination to enhance root colonization and plant growth promotion
Bibliographic record
Abstract
Strains of Bacillus species, plant growth-promoting rhizobacteria, have been commercialized as biofertilizers; they are ideal for this, because these species form spores that can be stored stably for a long time. However, for these spores to exert their full beneficial effects, they must germinate. The specific germination signals in the rhizosphere, particularly those from plant root exudates, remain largely unknown. Here, we investigated the germination signals from different growth states of cucumber ( Cucumis sativus ) for spores of B. velezensis SQR9 and B. subtilis NCIB 3610. We identified the corresponding germination receptors and compared them biochemically between the Bacillus species. Larger plants better stimulated spore germination. Five amino acids – L-isoleucine, L-ornithine, L-valine, L-serine, and β-alanine – were identified as spore germination signals. Combined application of a mixture of these amino acids with bacterial spores markedly enhanced the cucumber-growth-promoting properties of B. velezensis SQR9. The germination receptor for these amino acids was GerA in both Bacillus spp. Differences in spore germination efficiency between B. subtilis and B. velezensis may be attributable to variations in the GerA ligand-recognition sites. Expression of GerA from B. subtilis NCIB 3610 in B. velezensis SQR9 enhanced the spore germination rate of the latter. Our study highlights the pivotal role of amino acids in regulating spore germination of Bacillus and subsequent plant root colonization, emphasizing their potential to enhance the efficacy of Bacillus -based biofertilizers. Engineering of germination receptors is a promising approach to enhance the spore germination efficiency of biofertilizer strains.
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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) | 0.000 | 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) | 0.001 | 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".