Profiling of Metabolites of Bacillus spp. and Their Application in Sustainable Plant Growth Promotion and Biocontrol
Bibliographic record
Abstract
Bacillus spp. are well-characterized as efficient bioinoculants for sustainable plant growth promotion and biocontrol of phytopathogens. Members of this spp. exhibit the multifaceted beneficial traits that are involved in plant nutrition and antimicrobial activities against phytopathogens. Keeping in view their diverse potential, this study targeted the detailed characterization of three root-colonizing Bacillus strains namely B. amyloliquefaciens, B. subtilis, and B. tequilensis, characterized based on 16S rRNA sequencing homology. The strains exhibited better plant growth promotion and potent broad-spectrum antifungal activities and exerted 43–86% in-vitro inhibition of growth of eight fungal pathogens. All strains produced indole acetic acid (IAA) in the range of 0.067–0.147 μM and were positive for the production of extracellular enzymes such as cellulase, lipase, and protease. Ultra-performance Liquid Chromatography-Electrospray Ionization-Mass Spectrometry (UPLC-ESI-MS/MS) analysis revealed the production of antifungal metabolites (AFMs) such as surfactins, iturins, fengycins, macrolactins, bacillomycin-D, and catechol-based siderophore bacillibactin which were further confirmed by amplifying the genes involved in the biosynthesis of these antimicrobial lipopeptides. When compared for the amounts of different cyclic-peptides produced by three Bacillus strains, B. amyloliquefaciens SB-1 showed the most noticeable amounts of all the antifungal compounds. Plant experiment results revealed that inoculation with phytohormone producing Bacillus spp. strains demonstrated substantial growth improvement of wheat biomass, number of spikes, and dry weight of shoots and roots. Results of this study indicate the biocontrol and biofertilizer potential of Bacillus spp. for sustainable plant nutrient management, growth promotion, and effective biocontrol of crop plants, particularly cultivated in the South Asian region.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".