Transmitting silks of maize have a complex and dynamic microbiome
Bibliographic record
Abstract
In corn/maize, silks emerging from cobs capture pollen, and transmit resident sperm nuclei to eggs. There are > 20 million silks per U.S. maize acre. Fungal pathogens invade developing grain using silk channels, including Fusarium graminearum (Fg, temperate environments) and devastating carcinogen-producers (Africa/tropics). Fg contaminates cereal grains with mycotoxins, in particular Deoxynivalenol (DON), known for adverse health effects on humans and livestock. Fitness selection should promote defensive/healthy silks. Here, we report that maize silks, known as styles in other plants, possess complex and dynamic microbiomes at the critical pollen-fungal transmission interval (henceforth: transmitting style microbiome, TSM). Diverse maize genotypes were field-grown in two trial years. MiSeq 16S rRNA gene sequencing of 328 open-pollinated silk samples (healthy/Fg-infected) revealed that the TSM contains > 5000 taxa spanning the prokaryotic tree of life (47 phyla/1300 genera), including nitrogen-fixers. The TSM of silk tip tissue displayed seasonal responsiveness, but possessed a reproducible core of 7-11 MiSeq-amplicon sequence variants (ASVs) dominated by a single Pantoea MiSeq-taxon (15-26% of sequence-counts). Fg-infection collapsed TSM diversity and disturbed predicted metabolic functionality, but doubled overall microbiome size/counts, primarily by elevating 7-25 MiSeq-ASVs, suggestive of a selective microbiome response against infection. This study establishes the maize silk as a model for fundamental/applied research of plant reproductive microbiomes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".