The Cultured Microbiome of Pollinated Maize Silks Shifts after Infection with Fusarium graminearum and Varies by Distance from the Site of Pathogen Inoculation
Bibliographic record
Abstract
Styles transmit pollen-derived sperm nuclei from pollen to ovules, but also transmit environmental pathogens. The microbiomes of styles are likely important for reproduction/disease, yet few studies exist. Whether style microbiome compositions are spatially responsive to pathogens is unknown. The maize pathogen Fusarium graminearum enters developing grain through the style (silk). We hypothesized that F. graminearum treatment shifts the cultured transmitting silk microbiome (TSM) compared to healthy silks in a distance-dependent manner. Another objective of the study was to culture microbes for future application. Bacteria were cultured from husk-covered silks of 14 F. graminearum-treated diverse maize genotypes, proximal (tip) and distal (base) to the F. graminearum inoculation site. Long-read 16S sequences from 398 isolates spanned 35 genera, 71 species, and 238 OTUs. More bacteria were cultured from F. graminearum-inoculated tips (271 isolates) versus base (127 isolates); healthy silks were balanced. F. graminearum caused a collapse in diversity of ~20–25% across multiple taxonomic levels. Some species were cultured exclusively or, more often, from F. graminearum-treated silks (e.g., Delftia acidovorans, Klebsiella aerogenes, K. grimontii, Pantoea ananatis, Stenotrophomonas pavanii). Overall, the results suggest that F. graminearum alters the TSM in a distance-dependent manner. Many isolates matched taxa that were previously identified using V4-MiSeq (core and F. graminearum-induced), but long-read sequencing clarified the taxonomy and uncovered greater diversity than was initially predicted (e.g., within Pantoea). These isolates represent the first comprehensive cultured collection from pathogen-treated maize silks to facilitate biocontrol efforts and microbial marker-assisted breeding.
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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".