Cotton microbiome profiling and Cotton Leaf Curl Disease (CLCuD) suppression through microbial consortia associated with Gossypium arboreum
Bibliographic record
Abstract
The failure of breeding strategies has caused scientists to shift to other means where the new approach involves exploring the microbiome to modulate plant defense mechanisms against Cotton Leaf Curl Disease (CLCuD). The cotton microbiome of CLCuD-resistant varieties may harbor a multitude of bacterial genera that significantly contribute to disease resistance and provide information on metabolic pathways that differ between the susceptible and resistant varieties. The current study explores the microbiome of CLCuD-susceptible Gossypium hirsutum and CLCuD-resistant Gossypium arboreum using 16 S rRNA gene amplification for the leaf endophyte, leaf epiphyte, rhizosphere, and root endophyte of the two cotton species. This revealed that Pseudomonas inhabited the rhizosphere while Bacillus was predominantly found in the phyllosphere of CLCuV-resistant G. arboreum. Using salicylic acid-producing Serratia spp. and Fictibacillus spp. isolated from CLCuD-resistant G. arboreum, and guided by our analyses, we have successfully suppressed CLCuD in the susceptible G. hirsutum through pot assays. The applied strains exhibited less than 10% CLCuD incidence as compared to control group where it was 40% at 40 days post viral inoculation. Through detailed analytics, we have successfully demonstrated that the applied microbes serve as a biocontrol agent to suppress viral disease in Cotton.
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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".