Genomic and phenotypic profiling of an Artemisia endophyte: dual biostimulant and biocontrol activities in pea under Fusarium stress
Bibliographic record
Abstract
Objective To evaluate the plant growth-promoting and disease-suppressing potential of Serratia sp. AR11, an endophytic bacterium isolated from Artemisia absinthium, through phenotypic assessment and whole-genome analysis in pea (Pisum sativum) under normal and Fusarium-stress conditions. Materials and methods Greenhouse experiments were conducted to assess the effects of AR11 inoculation on shoot and root growth, biomass, chlorophyll content, and Fusarium-induced stunting. Whole-genome sequencing was performed using the PacBio SMRT platform, followed by functional annotation to identify genes related to nutrient metabolism, secondary metabolite biosynthesis, and stress adaptation. Biosafety assessment included screening for virulence and antibiotic resistance genes. Results AR11 inoculation significantly increased shoot and root length and biomass, while reducing Fusarium-induced stunting by over 70%. Under pathogen stress, treated plants showed a 67% increase in SPAD index compared to controls. Genome analysis revealed a complete 5.49 Mb circular genome with 5,175 protein-coding genes, including those for nitrogen metabolism, phosphate solubilization, siderophore production, and antifungal secondary metabolite biosynthesis. Additional features included stress-responsive regulatory systems, CRISPR-Cas loci, and DNA methylation patterns. No high-risk virulence or acquired antibiotic resistance genes were detected. Conclusion Serratia sp. AR11 is a safe, multifunctional endophytic bacterium with strong genomic and phenotypic evidence for enhancing pea growth and suppressing Fusarium-related diseases, making it a promising candidate for sustainable low-input agriculture.
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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".