Soybean Microbiome Recovery After Disruption is Modulated by the Seed and Not the Soil Microbiome
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 it