Integrated transcriptomics and metabolomics reveal induction of hierarchies of resistance genes in potato against late blight
Bibliographic record
Abstract
Late blight caused by Phytophthora infestans is a devastating disease affecting potato production worldwide. The quantitative resistance is durable, but the underlying molecular and biochemical mechanisms are poorly understood, limiting its application in breeding. Integrated transcriptomics and metabolomics approach was used for the first time to study the hierarchies of molecular events occurring, following inoculation of resistant and susceptible potato genotypes with P. infestans. RNA sequencing revealed a total of 4216 genes that were differentially expressed in the resistant than in the susceptible genotype. Genes that were highly expressed and associated with their biosynthetic metabolites that were highly accumulated, through metabolic pathway regulation, were selected. Quantitative real-time PCR was performed to confirm the RNA-seq expression levels. The induced leucine-rich repeat receptor-like kinases (LRR-RLKs) are considered to be involved in pathogen recognition. These receptor genes are considered to trigger downstream oxidative burst, phytohormone signalling-related genes, and transcription factors that regulated the resistance genes to produce resistance related metabolites to suppress the pathogen infection. It was noted that several resistance genes in metabolic pathways related to phenylpropanoids, flavonoids, alkaloids and terpenoid biosynthesis were strongly induced in the resistant genotypes. The pathway specific gene induction provided key insights into the metabolic reprogramming of induced defence responses in resistant genotypes.
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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) | 0.001 | 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".