Construction of Host Plant Insect-Resistance Mutant Library by High-Throughput CRISPR/Cas9 System and Identification of A Broad-Spectrum Insect Resistance Gene.

Insects pose significant challenges in cotton-producing regions. Here, they describe a high-throughput CRISPR/Cas9-mediated large-scale mutagenesis library targeting endogenous insect-resistance-related genes in cotton. This library targeted 502 previously identified genes using 968 sgRNAs, generated ≈2000 T0 plants and achieved 97.29% genome editing with efficient heredity, reaching upto 84.78%. Several potential resistance-related mutants (10% of 200 lines) their identified that may contribute to cotton-insect molecular interaction. Among these, they selected 139 and 144 lines showing decreased resistance to pest infestation and targeting major latex-like protein 423 (GhMLP423) for in-depth study. Overexpression of GhMLP423 enhanced insect resistance by activating the plant systemic acquired resistance (SAR) of salicylic acid (SA) and pathogenesis-related (PR) genes. This activation is induced by an elevation of cytosolic calcium [Ca2+ ]cyt flux eliciting reactive oxygen species (ROS), which their demoted in GhMLP423 knockout (CR) plants. Protein-protein interaction assays revealed that GhMLP423 interacted with a human epidermal growth factor receptor substrate15 (EPS15) protein at the cell membrane. Together, they regulated the systemically propagating waves of Ca2+ and ROS, which in turn induced SAR. Collectively, this large-scale mutagenesis library provides an efficient strategy for functional genomics research of polyploid plant species and serves as a solid platform for genetic engineering of insect resistance.

Detection of Hindwing Landmarks Using Transfer Learning and High-Resolution Networks.

Hindwing venation is one of the most important morphological features for the functional and evolutionary analysis of beetles, as it is one of the key features used for the analysis of beetle flight performance and the design of beetle-like flapping wing micro aerial vehicles. However, manual landmark annotation for hindwing morphological analysis is a time-consuming process hindering the development of wing morphology research. In this paper, we present a novel approach for the detection of landmarks on the hindwings of leaf beetles (Coleoptera, Chrysomelidae) using a limited number of samples. The proposed method entails the transfer of a pre-existing model, trained on a large natural image dataset, to the specific domain of leaf beetle hindwings. This is achieved by using a deep high-resolution network as the backbone. The low-stage network parameters are frozen, while the high-stage parameters are re-trained to construct a leaf beetle hindwing landmark detection model. A leaf beetle hindwing landmark dataset was constructed, and the network was trained on varying numbers of randomly selected hindwing samples. The results demonstrate that the average detection normalized mean error for specific landmarks of leaf beetle hindwings (100 samples) remains below 0.02 and only reached 0.045 when using a mere three samples for training. Comparative analyses reveal that the proposed approach out-performs a prevalently used method (i.e., a deep residual network). This study showcases the practicability of employing natural images-specifically, those in ImageNet-for the purpose of pre-training leaf beetle hindwing landmark detection models in particular, providing a promising approach for insect wing venation digitization.