Plant growth-promoting rhizobacteria enhance active ingredient accumulation in medicinal plants at elevated CO2 and are associated with indigenous microbiome
Bibliographic record
Abstract
Introduction Plant growth-promoting rhizobacteria (PGPR) and elevated CO 2 (eCO 2 ) have demonstrated their individual potential to enhance plant yield and quality through close interaction with rhizosphere microorganisms and plant growth. However, the efficacy of PGPR under eCO 2 on rhizosphere microbiome and, ultimately, plant yield and active ingredient accumulation are not yet fully understood. Methods This study investigated how the medicinal plant Pseudostellaria heterophylla ( P. heterophylla ) and its rhizosphere microbes respond to PGPR ( Bacillus subtilis and Pseudomonas fluorescens ) at eCO 2 (1,000 ppm). Results and Discussion It was found that the yield and active ingredient polysaccharides accumulation in the tuber of P. heterophylla were significantly increased by 38 and 253%, respectively. This promotion has been associated with increased root development and changes in the indigenous microbial community. Metagenomics analysis revealed a significant reduction in pathogenic Fusarium abundance in the rhizosphere. Potential biocontrol bacteria Actinobacteria and Proteobacteria were enriched, especially the genera Bradyrhizobium and Rhodanobacter . The reshaping of the rhizosphere microbiome was accompanied by the upregulation of biological pathways related to metabolite biosynthesis in the rhizosphere. These modifications were related to the promotion of the growth and productivity of P. heterophylla . Our findings highlighted the significant role played by PGPR in medicinal plant yield and active ingredient accumulation when exposed to eCO 2 .
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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".