Transcriptome analysis of maize reveals potential key genes involved in the response to belowground herbivore <i>Holotrichia parallela</i> larvae feeding
Bibliographic record
Abstract
The larvae of Holotrichia parallela, a destructive belowground herbivore, causes tremendous damages to maize plants. However, little is known if there are any defense mechanisms in maize roots to defend themselves against this herbivore. In the current research, we carried out RNA-sequencing to investigate the changes in gene transcription level in maize roots after H. parallela larvae infestation. A total of 644 up-regulated genes and 474 down-regulated genes was found. In addition, Gene ontology (GO) annotation analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Weighted gene co-expression network analysis (WGCNA) indicated that peroxidase genes may be the hub genes that regulate maize defenses to H. parallela larvae attack. We also found 105 transcription factors, 44 hormone-related genes, and 62 secondary metabolism-related genes within differentially expressed genes (DEGs). Furthermore, the expression profiles of 12 DEGs from the transcriptome analysis were confirmed by quantitative real-time PCR experiments. This transcriptome analysis provides insights into the molecular mechanisms of the underground defense in maize roots to H. parallela larvae attack and will help to select target genes of maize for defense against belowground herbivory.
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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 | 0.001 |
| 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) | 0.001 | 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".