Dual role of KHCO₃ priming: reduces seed-borne pathogens on sorghum seeds and promotes seed germination
Bibliographic record
Abstract
BACKGROUND: Seed-borne diseases have seriously affected the sustainability of sorghum cultivation in China as the demand for organic products in the winemaking industry has limited the use of chemical fungicides. RESULTS: This study conducted a comprehensive analysis of fungal diversity in sorghum seeds from three major growing regions in Guizhou Province. Using a combination of traditional tissue separation and high-throughput sequencing, we identified Colletotrichum, Fusarium, Cladosporium, and Alternaria as dominant fungi. Pathogenicity tests revealed that strains GD202206, GD202219, and GD202242 were pathogenic and were identified as C. sublineola through morphological and multi-locus phylogeny analysis (ITS、CAPDH、ACT、CHS-1 and TUB2). 16 fungicides for seed priming experiments with sorghum seeds, the results indicated that priming with KHCO₃ significantly enhanced germination of the sorghum seeds, with both indoor and outdoor emergence rates notably higher. Analysis of the fungal changes before and after KHCO₃ priming revealed a significant reduction in the abundance of the Colletotrichum genus. Additionally, KHCO₃ altered the community structure of fungi within the sorghum seeds, reducing population richness. Inter-generic relationships were rebalanced, with antagonism decreased and synergy increased following KHCO₃ treatment. Non-target metabolomic analysis indicated that KHCO₃ enhances sorghum seed germination via the phenylalanine and flavonoid pathways and exhibits antifungal properties through the cyanoamino acid metabolic pathway. CONCLUSION: This study identified C. sublineola as the primary pathogenic fungus carried by sorghum seeds. KHCO₃ treatment has a dual effect on sorghum seeds: on one hand, it suppresses pathogen transmission by reducing the abundance of the Colletotrichum genus; on the other hand, it promotes germination and seedling emergence, thereby enhancing both germination and emergence rates.
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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".