The native soil microbiome is critical for early root-associated microbiota assembly and canola growth
Bibliographic record
Abstract
BACKGROUND: The native soil microbiome contributes to regulating the root-associated microbiota, root morphology, and plant growth. Using two canola (Brassica napus L.) genotypes contrasting in root size (small-rooted NAM23 and large-rooted NAM37), we investigated how the native soil microbiome influences canola establishment. Plants were grown in rhizoboxes containing gamma-irradiated (microbiome dysbiosis) or untreated (healthy microbiome) soils for 14 days. We evaluated plant growth and profiled bacterial and fungal communities in unplanted soil, rhizosphere soil, and root samples via DNA amplicon sequencing. RESULTS: Soil irradiation inhibited canola early growth, severely reducing shoot fresh mass (8 to 10-fold), root fresh mass and root length (3 to 13-fold). As expected, irradiation reduced microbial diversity and altered microbial community structure. The absence of significant soil physicochemical changes post-irradiation suggests that microbiome dysbiosis, rather than nutrient depletion, was the primary driver of plant growth suppression in irradiated soil. This growth suppression correlated with the depletion of potentially beneficial taxa (e.g., Sphingomonas, Alternaria prunicola, Fusarium, Gibberella avenacea, and Humicola nigrescens) and/or the enrichment of detrimental taxa (e.g., Mucilaginibacter, Leifsonia, and Trichoderma atrobrunneum) in both soil and roots. The large-rooted NAM37 outperformed the small-rooted NAM23 only in healthy microbiome-intact soils, but this growth advantage was not observed in unhealthy microbiome-disrupted irradiated soils. CONCLUSIONS: Our findings directly demonstrate the critical role of a healthy soil microbiome in supporting canola establishment. The absence of growth disparities between genotypes in irradiated soil indicates that plant fitness is not attributed to fixed root phenotypes but a dynamic interplay between intrinsic root traits and the microbiome.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".