Soybean-SCN duel: Novel insight into Soybean’s Resistant Responses to <i>Heterodera glycines</i>
Bibliographic record
Abstract
Abstract Soybean cyst nematodes (SCN, Heterodera glycines Ichinohe) are a significant threat to soybean production globally, causing severe yield losses and necessitating the development of effective strategies to combat this devastating nematode disease. This study presents dual RNA-seq analyses of the three most common SCN-resistant lines (Plant Introduction (PI) 437654, 548402, and 88788) and the susceptible line Lee74 against the SCN HG type 1.2.5.7 to identify the mechanisms of resistance and virulence genes involved in resistance breakdown. Transcriptomic and pathway analyses reveal the activation of the phenylpropanoid pathway, MAPK signaling pathway, plant hormone signal transduction, and secondary metabolite pathways in the resistance mechanisms. PI 437654, which exhibited robust resistance (female index, FI=0%), demonstrated unique gene expression associated with cell wall reinforcement, oxidative enzymes, ROS scavengers, and Ca+2 sensors governing the salicylic acid (SA) biosynthesis process, indicating its key defense mechanism. Moreover, using different hosts with varying levels of immunity and a susceptible line provided insights into SCN pathogenesis and how H. glycine overcomes different layers of host immunity by modulating its virulence genes. This research provides novel insights into the molecular mechanisms underlying soybean-SCN interactions and identifies potential targets for developing strategies to manage this devastating nematode disease.
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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".