A survey of Methylobacterium species and strains reveals widespread production and varying profiles of cytokinin phytohormones
Bibliographic record
Abstract
Abstract Background Symbiotic Methylobacterium strains comprise a significant part of plant microbiomes. Their presence enhances plant productivity and stress resistance, prompting classification of these strains as plant growth-promoting bacteria (PGPB). Methylobacteria can synthesize unusually high levels of plant hormones, called cytokinins (CKs), including the most active form, trans-Zeatin (tZ). Results This study provides a comprehensive inventory of 46 representatives of Methylobacterium genus with respect to phytohormone production in vitro, including 16 CK forms, abscisic acid (ABA) and indole-3-acetic acid (IAA). High performance-liquid chromatography—tandem mass spectrometry (HPLC–MS/MS) analyses revealed varying abilities of Methylobacterium strains to secrete phytohormones that ranged from 5.09 to 191.47 pmol mL −1 for total CKs, and 0.46 to 82.16 pmol mL −1 for tZ. Results indicate that reduced methanol availability, the sole carbon source for bacteria in the medium, stimulates CK secretion by Methylobacterium . Additionally, select strains were able to transform L-tryptophan into IAA while no ABA production was detected. Conclusions To better understand features of CKs in plants, this study uncovers CK profiles of Methylobacterium that are instrumental in microbe selection for effective biofertilizer formulations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".