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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.000 | 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 it