IAA-producing rhizobacteria from chickpea (<i>Cicer arietinum</i>L.) induce changes in root architecture and increase root biomass
Bibliographic record
Abstract
Rhizobacteria promote and have beneficial effects on plant growth, making them useful to agriculture. Nevertheless, the rhizosphere of the chickpea plant has not been extensively examined. The aim of the present study was to select indole-3-acetic acid (IAA) producing rhizobacteria from the rhizosphere of chickpea plants for their potential use as biofertilizers. After obtaining a collection of 864 bacterial isolates, we performed a screen using the Salkowski reaction for the presence of auxin compounds (such as IAA) in bacterial Luria-Bertani supernatant (BLBS). Our results demonstrate that the Salkowski reaction has a greater specificity for detecting IAA than other tested auxins. Ten bacterial isolates displaying a wide range of auxin accumulation were selected, producing IAA levels of 5 to 90 μmol/L (according to the Salkowski reaction). Bacterial isolates were identified on the basis of 16S rDNA partial sequences: 9 isolates belonged to Enterobacter, and 1 isolate was classified as Serratia. The effect of BLBS on root morphology was evaluated in Arabidopsis thaliana. IAA production by rhizobacteria was confirmed by means of a DR5::GFP construct that is responsive to IAA, and also by HPLC-GC/MS. Finally, we observed that IAA secreted by rhizobacteria (i) modified the root architecture of A. thaliana, (ii) caused an increase in chickpea root biomass, and (iii) activated the green fluorescent protein (GFP) reporter gene driven by the DR5 promoter. These findings provide evidence that these novel bacterial isolates may be considered as putative plant-growth-promoting rhizobacteria modifying root architecture and increasing root biomass.
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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 | 0.000 |
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".