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Record W4411345061 · doi:10.1094/phytofr-12-24-0136-r

Survey of Maize Differential Gene Expression Upon Environmental Exposure to the Tar Spot Pathogen, <i>Phyllachora maydis</i>

2025· article· en· W4411345061 on OpenAlexaboutno aff
Emily M. Roggenkamp, Namrata Jaiswal, Matthew Helm, Addie Thompson, Martin I. Chilvers

Bibliographic record

VenuePhytoFrontiers™ · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetics and Plant Breeding
Canadian institutionsnot available
FundersAgricultural Research ServiceU.S. Department of AgricultureNational Institute of Food and AgricultureNational Science Foundation
KeywordsPathogenBiologytar (computing)Differential (mechanical device)GeneGeneticsEngineeringComputer science

Abstract

fetched live from OpenAlex

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 .

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.189
Teacher spread0.174 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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