Temporal proteomic profiling via 4D-DIA reveals early defense mechanisms and core resistance determinants in soybean against Phakopsora pachyrhizi
Bibliographic record
Abstract
Asian soybean rust, caused by Phakopsora pachyrhizi, is a devastating fungal disease threatening global soybean production, particularly in tropical regions where chemical control is increasingly unsustainable. This study employed cutting-edge 4D-DIA proteomics to investigate molecular defense mechanisms in resistant (SX6907) and susceptible (Tianlong 1) soybean cultivars during early infection (12 hpi and 3 dpi). We identified 12,852 proteins, with 1,510 differentially expressed proteins (DEPs) revealing genotype-specific responses. Resistant plants exhibited sustained upregulation of immune receptors (CRKs, LRR-RLKs), MAPK signaling components, and cell wall reinforcement proteins (peroxidases, XTHs), alongside dynamic modulation of calcium signaling and ROS homeostasis. These patterns suggest key pathways enriched in resistance may include phenylpropanoid biosynthesis, isoflavonoid production, and ER stress responses, while susceptible plants showed suppression of photosynthesis and defense pathways. Weighted Protein Co-expression Network Analysis(WPCNA) highlighted co-expression modules linked to resistance, potentially including NLR-mediated effector-triggered immunity. Crucially, DIR proteins and organelle-specific defense hubs (e.g., chloroplasts, nuclei) were implicated in rust resistance. Validation by qPCR confirmed concordance for 84% of tested DEPs. Our findings provide a protein-level blueprint of soybean rust resistance, identifying candidate targets for marker-assisted breeding and genetic engineering to develop durable resistant varieties, reducing reliance on fungicides.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".