Transcriptome Profiling in Hybrid Poplar Following Interactions with<i>Melampsora</i>Rust Fungi
Bibliographic record
Abstract
In natural conditions, plants are subjected to a combination of biotic stresses and often have to cope with simultaneous pathogen infections. In this report, we aim to understand the global transcriptional response of hybrid poplar NM6 (Populus nigra x P. maximowiczii) to infection by two biotrophic Melampsora fungi, Melampsora larici-populina and M. medusae f. sp. deltoidae. These pathogens triggered different responses after inoculation of poplar leaves. Transcript profiling using the GeneChip Poplar Genome Array revealed a total of 416 differentially expressed transcripts whose expression level was > or = twofold relative to controls. Interestingly, approximately half of the differentially expressed genes in infected leaves showed altered expression following interaction with either of the Melampsora spp. We also infected poplar leaves simultaneously with both Melampsora spp. to investigate potential interaction between the responses to the individual pathogens during a mixed infection. For this mixed inoculation, the number of differentially expressed transcripts increased to 648 and our analysis showed that infection with both fungi also induced a common set of genes. The genes induced after Melampsora spp. infection were mainly related to primary and secondary metabolic processes, cell-wall reinforcement and lignification, defense and stress-related mechanisms, and signal perception and transduction.
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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".