Assembly and potential transmission of the <i>Lens culinaris</i> seed microbiome
Bibliographic record
Abstract
Soil is an important source of bacteria and fungi for the plant, but seeds can also provide microbial inocula through heritable or stochastic assembly. Seed-associated microbial communities can potentially interact with the host plant through multiple generations. Here, we assessed the impact of two different soil types on the seed microbiome assembly of seven lentil (Lens culinaris) genotypes under environmentally controlled conditions and examined the vertical transmission of bacterial communities from seed to seed across two generations. Bulk soil microbiomes and seed microbiomes were characterized using high-throughput amplicon sequencing of the bacterial 16S rRNA gene. Our results revealed that bacterial communities in the two soils differed significantly and that bacterial communities associated with seeds were significantly impacted by genotype (15%) in one of the soils. Co-occurrence of amplicon sequence variants between generations suggests that members of the genera Cutibacterium, Methylobacterium, Sphingomonas, Streptococcus and Tepidimonas are transmitted and preserved in lentil genotypes irrespective of the soil in which they were grown. Increasing our knowledge of how microbial communities carried by seeds are assembled, transmitted and preserved offers a promising way for future breeding programs to consider microbial communities when selecting for more resilient and productive cultivars.
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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 | 0.001 | 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".