Draft genome sequence, disease-resistance genes, and phenotype of a <i>Paenibacillus terrae</i> strain (NK3-4) with the potential to control plant diseases
Bibliographic record
Abstract
Paenibacillus terrae NK3-4 is a plant growth-promoting rhizobacterium that may be useful for controlling plant diseases. We conducted a genomic analysis and identified the genes mediating antimicrobial functions. Additionally, an extracellular antifungal protein component was isolated and identified. The draft genome sequence was assembled into 54 contigs, with 5 458 568 bp and a G+C content of 47%. Moreover, 4 690 015 bp encoded 5090 proteins, 7 rRNAs, and 54 tRNAs. Forty-four genes involved in antimicrobial functions were detected. They mainly encode 19 non-ribosomal peptide synthetases (NRPSs); one polyketide synthase/NRPSs hybrid enzyme; four Zn-dependent metalloproteases; three antilisterial bacteriocin subtilosin biosynthesis proteins (AlbA); four serine proteases; five pectate lyases; three beta-glucanases; and four 1,4-beta-xylanases. These include four novel NRPSs that have not been found in any species of Paenibacillus. Furthermore, five proteins exhibiting antifungal activity were identified from the antifungal extracellular protein component based on MS/MS and the strain NK3-4 predicted protein library. On the basis of these features, we propose that strain NK3-4 represents a promising biocontrol agent for protecting plant from diseases. The draft genome sequence described herein may provide the genetic basis for the characterization of the molecular mechanisms underlying the biocontrol functions. It may also facilitate the development of rational strategies for improving the strain.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".