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AQPs Characteristics of Megoura crassicauda and Their Expression Changes in Response to High Relative Humidity Stress
Scientia Agricultura Sinica 2024, 57(20): 4057-4070
Published: 16 October 2024
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【Objective】

At present, crop pests are more adaptable to high relative humidity. Aquaporin (AQP) is a membrane protein that is necessary for insects to maintain water balance in the body. The objectives of this study are to screen two McAQP sequence structures by transcriptome sequencing through high relative humidity stress, clarify the AQP characteristics of Megoura crassicauda and the expression changes in response to high relative humidity stress combined with real-time fluorescence quantitative PCR (qRT-PCR) verification, and to provided a theoretical basis for further exploring the gene function of AQP.

【Method】

The environmental relative humidity (RH) of 60%, 75% and 90% were set to cultivate M. crassicauda. The growth and development were observed, the relative expression of genes and the content of related substances in the body were detected. Based on two McAQP sequences (named as McAQP X1 and McAQP X2, respectively) obtained from comparative transcriptome sequencing of M. crassicauda, the physicochemical property, sequence structure, and the homology with other insects were analyzed by bioinformatics methods. Finally, qRT-PCR was used to determine the relative expression of AQP X1, AQP X2, Vg and VgR under long-term stable high relative humidity stress and 24 h emergency high relative humidity stress.

【Result】

Compared with RH 60% and RH 75%, the fecundity of M. crassicauda under RH 90% was significantly reduced, but there was no significant difference in development duration and survival rate. With the increase of relative humidity, the water content of M. crassicauda decreased, and the special phenotype phenomena such as body color change and long wing appeared. The molecular weights of McAQP X1 and McAQP X2 are 33.89 and 28.94 kDa, the theoretical isoelectric points are 5.36 and 5.32, and the protein lengths are 308 and 272 aa, respectively. On the multistage structure, McAQP is arranged by six long alpha helices via counterclockwise rotation to form a barrel channel, whereas four monomers form a tetramer to exercise function. The relative expression of McAQPs quantified by qRT-PCR showed that both McAQP X1 and McAQP X2 were up-regulated with increasing relative humidity, which was generally consistent with the transcriptome sequencing results. Meanwhile, under long-term stable high humidity stress, both Vg and VgR were up-regulated under RH 75% and down-regulated under RH 90%. Under 24 h emergency high humidity stress, Vg expression level was down-regulated with increasing humidity, while VgR was up-regulated under RH 90%.

【Conclusion】

High humidity environment affects the reproduction, water content and body color of M. crassicauda. The aquaporin sequence structure in subfamily II is relatively conservative, without strong species-specific differentiation. NPA site is important for the function of aquaporin, and the asparagine residue plays a structural role for water molecules through the central channel. It is speculated that the expression change of AQPs may be an important means for M. crassicauda to cope with humidity changes.

Open Access Research Article Issue
Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition
Plant Phenomics 2024, 6: 0208
Published: 31 July 2024
Abstract Collect

Accurate identification of plant diseases is important for ensuring the safety of agricultural production. Convolutional neural networks (CNNs) and visual transformers (VTs) can extract effective representations of images and have been widely used for the intelligent recognition of plant disease images. However, CNNs have excellent local perception with poor global perception, and VTs have excellent global perception with poor local perception. This makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition tasks. In this paper, we propose a local and global feature-aware dual-branch network, named LGNet, for the identification of plant diseases. More specifically, we first design a dual-branch structure based on CNNs and VTs to extract the local and global features. Then, an adaptive feature fusion (AFF) module is designed to fuse the local and global features, thus driving the model to dynamically perceive the weights of different features. Finally, we design a hierarchical mixed-scale unitguided feature fusion (HMUFF) module to mine the key information in the features at different levels and fuse the differentiated information among them, thereby enhancing the model's multiscale perception capability. Subsequently, extensive experiments were conducted on the AI Challenger 2018 dataset and the self-collected corn disease (SCD) dataset. The experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the AI Challenger 2018 dataset and the SCD dataset, with accuracies of 88.74% and 99.08%, respectively.

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