Deep sequencing leads to the identification of eukaryotic translation initiation factor 5A as a key element in <i>Rsv1</i> ‐mediated lethal systemic hypersensitive response to <i>Soybean mosaic virus</i> infection in soybean
Bibliographic record
Abstract
Rsv1, a single dominant resistance locus in soybean, confers extreme resistance to the majority of Soybean mosaic virus (SMV) strains, but is susceptible to the G7 strain. In Rsv1-genotype soybean, G7 infection provokes a lethal systemic hypersensitive response (LSHR), a delayed host defence response. The Rsv1-mediated LSHR signalling pathway remains largely unknown. In this study, we employed a genome-wide investigation to gain an insight into the molecular interplay between SMV G7 and Rsv1-genotype soybean. Small RNA (sRNA), degradome and transcriptome sequencing analyses were used to identify differentially expressed genes (DEGs) and microRNAs (DEMs) in response to G7 infection. A number of DEGs, DEMs and microRNA targets, and the interaction network of DEMs and their target mRNAs responsive to G7 infection, were identified. Knock-down of one of the identified DEGs, the eukaryotic translation initiation factor 5A (eIF5A), diminished the LSHR and enhanced viral accumulation, suggesting the essential role of eIF5A in the G7-induced, Rsv1-mediated LSHR signalling pathway. This work provides an in-depth genome-wide analysis of high-throughput sequencing data, and identifies multiple genes and microRNA signatures that are associated with the Rsv1-mediated LSHR.
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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 | 0.001 |
| 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".