Employing Genomic Tools to Explore the Molecular Mechanisms behind the Enhancement of Plant Growth and Stress Resilience Facilitated by a Burkholderia Rhizobacterial Strain
Bibliographic record
Abstract
The rhizobacterial strain BJ3 showed 16S rDNA sequence similarity to species within the Burkholderia genus. Its complete genome sequence revealed a 97% match with Burkholderia contaminans and uncovered gene clusters essential for plant-growth-promoting traits (PGPTs). These clusters include genes responsible for producing indole acetic acid (IAA), osmolytes, non-ribosomal peptides (NRPS), volatile organic compounds (VOCs), siderophores, lipopolysaccharides, hydrolytic enzymes, and spermidine. Additionally, the genome contains genes for nitrogen fixation and phosphate solubilization, as well as a gene encoding 1-aminocyclopropane-1-carboxylate (ACC) deaminase. The treatment with BJ3 enhanced root architecture, boosted vegetative growth, and accelerated early flowering in Arabidopsis. Treated seedlings also showed increased lignin production and antioxidant capabilities, as well as notably increased tolerance to water deficit and high salinity. An RNA-seq transcriptome analysis indicated that BJ3 treatment significantly activated genes related to immunity induction, hormone signaling, and vegetative growth. It specifically activated genes involved in the production of auxin, ethylene, and salicylic acid (SA), as well as genes involved in the synthesis of defense compounds like glucosinolates, camalexin, and terpenoids. The expression of AP2/ERF transcription factors was markedly increased. These findings highlight BJ3’s potential to produce various bioactive metabolites and its ability to activate auxin, ethylene, and SA signaling in Arabidopsis, positioning it as a new Burkholderia strain that could significantly improve plant growth, stress resilience, and immune function.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".