Transcriptome Analysis of Maize Leaf Systemic Symptom Infected by Bipolaris zeicola
Bibliographic record
Abstract
Bipolaris zeicola is a fungal pathogen that causes Northern corn leaf spot (NCLS), which is a serious foliar disease in maize and one of the most significant pathogens affecting global food security. Here, we report a genome-wide transcriptional profile analysis using next-generation sequencing (NGS) of maize leaf development after inoculation with B. zeicola. We performed High-Throughput Digital Gene Expression analysis to identify differentially expressed genes (DEGs) in resistant inbred Mo17 lines after infection with B. zeicola at four successive disease development stages--CP (contact period), PP (penetration period), IP (incubation period), and DP (disease period); the expression of the genes was compared with those in a CK (mock-treatment) control. In addition, a sensitive maize line (Zheng58) was used for the comparisons with the Mo17. Among all tested genes, 466 differentially expressed genes were identified in all libraries, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of these genes suggested that they are involved in many biological processes related to systemic symptom development, such as plant hormone signal transduction, starch and sucrose metabolism, phenylpropanoid biosynthesis and photosynthesis. Our systematic analysis provides comprehensive transcriptomic information regarding systemic symptom development in fungal-infected plants. This information will help in furthering our understanding of the detailed mechanisms of plant responses to fungal infection.
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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 | 0.000 | 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".