Mass Spectrometric Analysis Reveals Remnants of Host–Pathogen Molecular Interactions at the Starch Granule Surface in Wheat Endosperm
Bibliographic record
Abstract
The starch granules of wheat seed are solar energy-driven deposits of fixed carbon and, as such, present themselves as targets of pathogen attack. The seed's array of antimicrobial proteins, peptides, and small molecules comprises a molecular defense against penetrating pathogens. In turn, pathogens exhibit an arsenal of enzymes to facilitate the degradation of the host's endosperm. In this context, the starch granule surface is a relatively unexplored domain in which unique molecular barriers may be deployed to defend against and inhibit the late stages of infection. Therefore, it was compelling to explore the starch granule surface in mature wheat seed, which revealed evidence of host-pathogen molecular interactions that may have occurred during grain development. In this study, starch granules from the soft wheat Triticum aestivum cv. AC Andrew and hard wheat T. turgidum durum were isolated and water washed 20 times, and their surface proteins were digested in situ with trypsin. The peptides liberated into the supernatant and the peptides remaining at the starch granule surface were separately examined. In this way, we demonstrated that the identified proteins have a strong affinity for the starch granule surface. Proteins with known antimicrobial activity were identified, as well as several proteins from the plant pathogens Agrobacterium tumefaciens, Pectobacterium carotovorum, Fusarium graminearum, Magnaporthe grisea, Xanthomonas axonopodis, and X. oryzae. Although most of these peptides corresponded to uncharacterized hypothetical proteins of fungal pathogens, several peptide fragments were identical to cytosolic and membrane proteins of specific microbial pathogens. During development and maturation, wheat seed appeared to have resisted infection and lysed the pathogens where, upon desiccation, the molecular evidence remained fixed at the starch granule surface.
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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 | 0.001 | 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".