Rhizospheric Bacterial Distribution Influencing the Accumulation of Isoflavones, Phenolics, Flavonoids, and Antioxidant Activity in Soybean Roots Within Hydroponic System
Bibliographic record
Abstract
This study investigates how root color in soybeans affects isoflavone composition, rhizosphere bacterial diversity, total phenolics, total flavonoids, and antioxidant activity under a hydroponic cultivation system. Notably, soybean-brown roots (SBRs) accumulated significantly higher contents of isoflavones, exhibiting approximately a 14.9-fold increase in total glycosides (141.75 to 2121.59 µg/g), 7.3-fold increase in total malonyl-β-glycosides (127.52 to 930.45 µg/g), 2.8-fold increase in total aglycones (1825.90 to 5145.21 µg/g), and 3.9-fold increase in total isoflavones (2095.16 to 8197.26 µg/g) than soybean-white roots (SWRs). Isolated rhizosphere bacteria profiling revealed γ-Proteobacteria as the predominant class in both root types, constituting 77.6% and 73.9% of the bacterial community in SWRs and SBRs, respectively. However, SBRs supported a more diverse bacterial ecosystem, harboring thirteen genera compared to only eight genera in SWRs. Enhanced total phenolics, total flavonoids, and radical scavenging activity were also associated with the SBRs. These findings shed light on the dynamic interplay between root traits, bacterial interactions, and secondary metabolite biosynthesis in hydroponically grown soybeans. This work not only advances our understanding of plant root–microbiome–metabolite relationships but also offers a novel approach to exploring the potential of enhancing secondary metabolites in soybean plants through precision cultivation.
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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".