Survey of Maize Differential Gene Expression Upon Environmental Exposure to the Tar Spot Pathogen, <i>Phyllachora maydis</i>
Bibliographic record
Abstract
Tar spot of maize, caused by Phyllachora maydis, is an emerging threat to crop production across the United States and Canada. Current effective management of the disease includes application of fungicides and use of partially resistant maize varieties. Several studies have focused on mapping of tar spot-resistant loci from maize diversity panels. However, no additional analyses have been reported to further our understanding of the maize defense response to P. maydis. Therefore, prior to the availability of inoculation procedures, we performed transcriptome sequencing from maize leaf tissue exposed to P. maydis from a tar spot-infested field and assessed differential gene transcript expression. Over the course of disease development, leaves were sampled at two time points: 10 and 24 days post-exposure. Differentially expressed genes (DEGs) were determined by comparing gene transcript expression of exposed to non-exposed samples at the respective time points. These experiments revealed 3,160 significant DEGs at 10 days post-exposure and 3,953 significant DEGs at 24 days post-exposure. Further examination of the DEGs revealed transcript enrichment in Gene Ontology biological processes and Kyoto Encyclopedia of Genes and Genomes pathways involved in defense response, photosynthesis, cell wall biogenesis, and plant secondary metabolite biosynthesis. Importantly, several DEGs were previously implicated in having a putative role in P. maydis resistance. Furthermore, construction of gene regulatory networks revealed several transcription factors that may function in the regulation of maize defense responses to P. maydis. This study provides an initial survey of maize responses to P. maydis and identifies candidate genes that may be important in host–pathogen interactions. [Formula: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .
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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 | 0.000 | 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) | 0.000 | 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 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".