Poplar defense against insects: genome analysis, full‐length cDNA cloning, and transcriptome and protein analysis of the poplar Kunitz‐type protease inhibitor family
Bibliographic record
Abstract
*Kunitz protease inhibitors (KPIs) feature prominently in poplar defense responses against insects. The increasing availability of genomics resources enabled a comprehensive analysis of the poplar (p)KPI family. *Using genome analysis, expressed sequence tag (EST) mining and full-length (FL)cDNA cloning we established an inventory and phylogeny of pKPIs. Microarray and real-time PCR analyses were used to profile pKPI gene expression following real or simulated insect attack. Proteomics of insect midgut content was used to monitor stability of pKPI protein. *We identified 31 pKPIs in the genome and validated gene models by EST mining and cloning of 41 unique FLcDNAs. Genome organization of the pKPI family, with six poplar-specific subfamilies, suggests that tandem duplications have played a major role in its expansion. pKPIs are expressed throughout the plant and many are strongly induced by insect attack, although insect-specific signals seem initially to suppress the tree pKPI response. We found substantial peptide coverage for a potentially intact pKPI protein in insect midgut after eating poplar leaves. *These results highlight the complexity of an important defense gene family in poplar with regard to gene family size, differential constitutive and insect-induced gene expression, and resilience of at least one pKPI protein to digestion by herbivores.
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 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".