Arabidopsis-Based Dual-Layered Biological Network Analysis Elucidates Fully Modulated Pathways Related to Sugarcane Resistance on Biotrophic Pathogen Infection
Bibliographic record
Abstract
We assembled a dual-layered biological network to study the roles of resistance gene analogs (RGAs) in the resistance of sugarcane to infection by the biotrophic fungus causing smut disease. Based on sugarcane-Arabidopsisorthology, the modeling used metabolic and protein-protein interaction (PPI) data fromArabidopsis thaliana(from Kyoto Encyclopedia of Genes and Genomes (KEGG) and BioGRID databases) and plant resistance curated knowledge for Viridiplantae obtained through text mining of the UniProt/SwissProt database. With the network, we integrated functional annotations and transcriptome data from two sugarcane genotypes that differ significantly in resistance to smut and applied a series of analyses to compare the transcriptomes and understand both signal perception and transduction in plant resistance. We show that the smut-resistant sugarcane has a larger arsenal of RGAs encompassing transcriptionally modulated subnetworks with other resistance elements, reachinghubproteins of primary metabolism. This approach may benefit molecular breeders in search of markers associated with quantitative resistance to diseases in non-model systems.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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".