Plant growth-promoting Bacillus strains modulate early soybean development via proteome remodeling
Bibliographic record
Abstract
Plant adaptation to environmental stress involves tightly regulated cellular, molecular, and biochemical responses. Among these, microbe-assisted strategies have gained attention, particularly the role of the plant microbiome (phytomicrobiome) in promoting growth and stress resilience. Soybean (Glycine max), a major agricultural crop, actively recruits beneficial microbes through root-secreted secondary metabolites, fostering symbiotic interactions with endophytic bacteria. However, the direct and indirect impacts of root-associated endophytes on plant development remain incompletely understood. In this study, we investigated three Bacillus strains (HT1, HT2, and HT3) isolated previously from the soybean root microbiome.16 S rRNA analysis indicates that HT1 and HT2 are closely related to the Bacillus velezensis and HT3 to the Bacillus thuringiensis lineage although more detailed analyses are warranted for genus identification. These strains were selected for their demonstrated plant growth-promoting and biocontrol activities. Bacillus-HT1 and HT2 significantly enhanced soybean seed germination, while Bacillus-HT3 promoted leaf area expansion significantly compared to the control, indicating strain-specific developmental effects. To elucidate the molecular basis of these effects, we conducted shotgun proteomic profiling of soybean leaves. Enrichment analysis revealed distinct functional signatures, with Bacillus HT1 and HT2 associated with pathways linked to cellular component organization, microtubule dynamics, and organelle function, and Bacillus-HT3 inducing broader enrichment of photosynthesis, chloroplast organization, and biosynthetic processes. These findings suggest that HT1 and HT2 promote early developmental transitions, while HT3 enhances vegetative growth through large-scale metabolic reprogramming. Notably, proteins such as anthranilate synthase and proteasome subunit alpha type were differentially abundant, pointing to the potential involvement of auxin biosynthesis and ubiquitin-proteasome-mediated regulation but, the actual roles of these pathways remain to be validated. These findings provide mechanistic insights into how specific Bacillus strains modulate soybean development at the molecular level and highlight their potential for use as bio-inoculants to enhance crop productivity and resilience under stress conditions.
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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".