Soybean Leaf Proteomic Profile Influenced by Rhizobacteria Under Optimal and Salt Stress Conditions
Bibliographic record
Abstract
Soil salinity is a major abiotic stressor inhibiting plant growth and development by affecting a range of physiological processes. Plant growth promoting rhizobacteria (PGPR) are considered a sustainable option for alleviation of stress and enhancement of plant growth, yet their mode of action is complex and largely unexplored. In this study, an untargeted proteomic approach provided insights into growth and stress response mechanisms elicited in soybean plants by Rhizobium sp. SL42 and Hydrogenophaga sp. SL48 and co-inoculated with Bradyrhizobium japonicum 532C. The plants were grown under optimal and salt-stressed conditions up to their mid-vegetative stage; shoot growth variables were increased in the bacteria-treated plants. Shotgun proteomics of soybean leaf tissue revealed that a number of proteins related to plant growth and stress tolerance were modulated in the bacterial inoculation treatments. Several key proteins involved in major metabolic pathways of photosynthesis, respiration, and photorespiration were upregulated. These include photosystem I psaK, Rubisco subunits, glyceraldehyde-3-phosphate dehydrogenase, succinate dehydrogenase, and glycine decarboxylase. Similarly, stress response proteins such as catalase and glutathione S-transferase (antioxidants), proline-rich precursor protein (osmolyte), and NADP-dependent malic enzyme (linked to ABA signaling) were increased under salt stress. The functions of proteins related to plant growth and stress adaptation led to an expanded understanding of plant-microbe interactions. These findings suggest that the PGPR strains regulated proteome expression in soybean leaves through multiple signaling pathways, thereby inducing salinity tolerance, and improving plant growth in the presence of this abiotic stress challenge. Data are available via ProteomeXchange with identifier PXD025596.
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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".