Soybean Microbiome Recovery After Disruption is Modulated by the Seed and Not the Soil Microbiome
Bibliographic record
Abstract
Endophytic microbiomes of healthy seed form a symbiotic relationship with their host. Seed and environment are sources of microbes that colonize the developing plant; however, the influence of each remains unclear. Here, using irradiation combined with surface sterilization to generate near-axenic seed with disrupted and reduced microbiomes, we contrasted the colonization potential of seed and soil microbiomes. We hypothesized that the seed microbiome would be the primary colonizer of the plant endophytic compartments. Our experimental design comprised four treatments, using soybean as a model plant: (i) nearly axenic seed growing in a sterile environment, (ii) nonaxenic seed inoculated with a microbial soil extract, (iii) nearly axenic seed inoculated with a microbial seed extract, and (iv) nearly axenic seed inoculated with a microbial soil extract. After 14 days of growth, plants were harvested, and DNA was extracted from the shoot, roots, and rhizosphere and subjected to 16S ribosomal RNA gene amplicon sequencing, quantitative PCR quantification of the total community, and functional genes involved in the N cycle. Community dynamics were similar for most treatments within their respective compartments, except for the soil treatment, where rhizosphere and root microbiomes differed from other treatments, suggesting that the soil microbiome colonizes the belowground compartment efficiently only when the seed microbiome is severely disrupted. For the shoot, all treatments resembled the seed microbiome treatment, suggesting that the seedborne bacteria colonize the aboveground compartment preferentially. Our results highlight the primacy of the seed microbiome over the soils during early colonization, putting seed microbes as potential candidates of microbiome engineering efforts.
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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".