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Cited by 300

The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.

Insect Response to Plant Defensive Protease Inhibitors

Open access
Zhu-Salzman, Keyan, Zeng, Rensen·2015·Annual Review of Entomology

Abstract

Plant protease inhibitors (PIs) are natural plant defense proteins that inhibit proteases of invading insect herbivores. However, their anti-insect efficacy is determined not only by their potency toward a vulnerable insect system but also by the response of the insect to such a challenge. Through the long history of coevolution with their host plants, insects have developed sophisticated mechanisms to circumvent antinutritional effects of dietary challenges. Their response takes the form of changes in gene expression and the protein repertoire in cells lining the alimentary tract, the first line of defense. Research in insect digestive proteases has revealed the crucial roles they play in insect adaptation to plant PIs and has brought about a new appreciation of how phytophagous insects employ this group of molecules in both protein digestion and counterdefense. This review provides researchers in related fields an up-to-date summary of recent advances.

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Crossref · 10.1146/annurev-ento-010814-020816

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Metadata

Authors
Zhu-Salzman, K., & Zeng, R.
Venue
Annual Review of Entomology
Published
2015-01-07
Volume
60
Issue
1
Pages
233-252
ISSN
  • 0066-4170 (ISSN-L)
  • 1545-4487
OpenAlex
W2156233383
Cited by
300
References
65
FWCI
2.43
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Insect Response to Plant Defensive Protease Inhibitors. https://app.cochat.ai/papers/10.1146/annurev-ento-010814-020816
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Zhu-Salzman, K., & Zeng, R. (2015). Insect Response to Plant Defensive Protease Inhibitors. Annual Review of Entomology. https://doi.org/10.1146/annurev-ento-010814-020816

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.