Isolation and characterization of indigenous endophytic bacteria associated with leaves of switchgrass (<i>Panicum virgatum </i>L.) cultivars
Bibliographic record
Abstract
AIMS: To isolate and characterize indigenous bacterial endophytes from cultivars of switchgrass and study their antimicrobial and growth promoting potential. METHODS AND RESULTS: The diversity, molecular and biochemical characterizations of indigenous and culturable bacterial endophytes residing in leaves of switchgrass have not been studied previously. This study describes the characterization of 31 bacterial endophytes from three switchgrass cutlivars: Cave-in Rock, Blue Jacket and Tecumseh. Molecular and phylogenetic analysis based on the 16S rRNA sequence grouped the endophytes into eight different taxa that shared high homology of 98-99% with other known sequences. Bacterial endophytes were identified as Microbacterium testaceum, Curtobacterium flaccumfaciens, Bacillus subtilis and Bacillus pumilus, Pseudomonas fluorescens, Sphingomonas parapaucimobilis, Serratia sp. and Pantoea ananatis. Some endophytes were detected in switchgrass seeds and in plants that originated from seeds collected a year earlier, confirming vertical transmission to the next generation of the host. Selected endophytes produced cellulases and were capable of solubilizing inorganic phosphorus. Analysis of cell-free culture filtrate of selected strains using direct infusion orbitrap mass spectrometry confirmed the presence of several well-characterized lipopeptide toxins and phytohormones. Re-inoculation of the roots of switchgrass seedlings with endophytes singly or combined confirmed their migration to the upper aerial parts of the plant. CONCLUSIONS: Our findings suggest that switchgrass leaves harbour a diversity of bacterial endophytes, some of which could potentially be applied as growth promoting bacteria. SIGNIFICANCE AND IMPACT OF THE STUDY: This is the first report on the characterization of indigenous bacterial endophytes and their potential use as biofertilizers.
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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) | 0.000 | 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".