Unveiling molecular mechanisms and candidate genes for goss’s bacterial wilt and leaf blight resistance in corn through RNA-Seq analysis
Bibliographic record
Abstract
Abstract Goss’s bacterial wilt and leaf blight (GWLB) induced by Clavibacter nebraskensis poses a significant threat to corn production in Canada and the US. We conducted RNA-Seq analyses on two corn hybrid lines with contrasting levels of susceptibility to GWLB, 447 (susceptible) and 450 (resistant), to elucidate the molecular mechanisms underlying corn resistance to the disease. The two selected corn lines were subjected to inoculation with two isolates of C. nebraskensis exhibiting distinct levels of aggressiveness: DOAB232 (DOAB), a weakly aggressive isolate and Cn14-5-1 (BACT), a highly aggressive one, or were left untreated (CTL) Total RNA was isolated from leaf tissue in three biological replicates for each hybrid x treatment combination five days post-inoculation (dpi), a timepoint selected to capture the early transcriptional response to pathogen infection. RNA-sequencing was performed following poly-A tail isolation of the RNA on all 18 samples (6 combinations * 3 replicates). Following data cleaning and filtering of the raw RNA-seq data, unsupervised clustering, differential expression and pathway analysis was conducted. Our analysis revealed extensive transcriptional reprogramming in both resistant and susceptible lines, particularly in the susceptible corn line (447) when inoculated with the either the weak or aggressive strain of C. nebraskensis, but also in the resistant corn line when inoculated with the aggressive bacterial isolate. Inoculation of the resistant corn line (450) to the aggressive C. nebraskensis isolate, led to increased expression of photosynthesis-related and defense-associated genes, and downregulation of secondary metabolism and stress responses pathways, while inoculation of the susceptible corn line (447) with the weakly aggressive C. nebraskensis isolate led to upregulation of defense-associated genes. The functions of some of the top differentially expressed genes in each comparison are discussed within the context of understanding the molecular underpinnings of corn resistance to GWLB disease to identify targets for genetic enhancement towards higher disease resistance levels.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".