Indoxacarb triggers autophagy and apoptosis through ROS accumulation mediated by oxidative phosphorylation in the midgut of Bombyx mori.

Indoxacarb has been widely utilized in agricultural pest management, posing a significant ecological threat to Bombyx mori, a non-target economic insect. In the present study, short-term exposure to low concentration of indoxacarb significantly suppressed the oxidative phosphorylation pathway, and resulted in an accumulation of reactive oxygen species (ROS) in the midgut of B. mori. While, the ATP content exhibited a declining trend but there was no significant change. Moreover, indoxacarb also significantly altered the transcription levels of six autophagy-related genes, and the transcription levels of ATG2, ATG8 and ATG9 were significantly up-regulated by 2.56-, 1.90-, and 3.36-fold, respectively. The protein levels of ATG8-I and ATG8-II and MDC-stained frozen sections further suggested an increase in autophagy. Furthermore, the protein level and enzyme activity of CASP4 showed a significant increase in accordance with the transcription levels of apoptosis-related genes, indicating the activation of the apoptotic signaling pathway. Meanwhile, the induction of apoptosis signals in the midgut cells triggered by indoxacarb was confirmed through TUNEL staining. These findings suggest that indoxacarb can promote the accumulation of ROS by inhibiting the oxidative phosphorylation pathway, thereby inducing autophagy and apoptosis in the midgut cells of B. mori.

JH degradation pathway participates in hormonal regulation of larval development of Bombyx mori following λ-cyhalothrin exposure.

λ-Cyhalothrin (λ-cyh), a widely utilized pyrethroid insecticide, poses serious threats to non-target organisms due to its persistence nature in the environment. Exposure to low concentrations of λ-cyh has been observed to result in prolonged larval development in Bombyx mori, leading to substantial financial losses in sericulture. The present study was undertaken to elucidate the underlying mechanisms for prolonged development caused by λ-cyh (LC10) exposure. The results showed that the JH Ⅲ titer was significantly increased at 24 h of λ-cyh exposure, and the JH interacting genes Methoprene-tolerant 2, Steroid Receptor Co-activator, Krüppel-homolog 1, and JH binding proteins were also up-regulated. Although the target of rapamycin (Tor) genes were induced by λ-cyh, the biosynthesis of JH in the corpora allata was not promoted. Notably, 13 JH degradation genes were found to be significantly down-regulated in the midgut of B. mori. The mRNA levels and enzyme activity assays indicated that λ-cyh had inhibitory effects on JH esterase, JH epoxide hydrolase, and JH diol kinase (JHDK). Furthermore, the suppression of JHDK (KWMTBOMO01580) was further confirmed by both western blot and immunohistochemistry. This study has offered a comprehensive perspective on the mechanisms underlying the prolonged development caused by insecticides, and our results also hold significant implications for the safe production of sericulture.

Low concentration of indoxacarb interferes with the growth and development of silkworm by damaging the structure of midgut cells.

As an important economic insect, Bombyx mori plays an essential role in the development of the agricultural economy. Indoxacarb, a novel sodium channel blocker insecticide, has been widely used for the control of various pests in agriculture and forestry, and its environmental pollution caused by flight control operations has seriously affected the safe production of sericulture in recent years. However, the lethal toxicity and adverse effects of indoxacarb on silkworm remain largely unknown. In this study, the toxicity of indoxacarb on the 5th instar larvae of silkworm was determined, with an LC50 (72 h) of 2.07 mg/L. Short-term exposure (24 h) to a low concentration of indoxacarb (1/2 LC50) showed significantly reduced body weight and survival rate of silkworm larvae. In addition, indoxacarb also led to decreased cocoon weight and cocoon shell weight, but had no significant effects on pupation, adult eclosion, and oviposition. Histopathological and ultrastructural analysis indicated that indoxacarb could severely damage the structure of the midgut epithelial cells, and lead to physiological impairment of the midgut. A total of 3883 differentially expressed genes (DEGs) were identified by midgut transcriptome sequencing and functionally annotated using GO and KEGG. Furthermore, the transcription level and enzyme activity of the detoxification related genes were determined, and our results suggested that esterases (ESTs) might play a major role in metabolism of indoxacarb in the midgut of B. mori. Future studies to examine the detoxification or biotransformation function of candidate genes will greatly enhance our understanding of indoxacarb metabolism in B. mori. The results of this study provide a theoretical basis for elucidating the mechanism of toxic effects of indoxacarb on silkworm by interfering with the normal physiological functions of the midgut.

IAS-NET: Joint intraclassly adaptive GAN and segmentation network for unsupervised cross-domain in neonatal brain MRI segmentation.

PURPOSE: In neonatal brain magnetic resonance image (MRI) segmentation, the model we trained on the training set (source domain) often performs poorly in clinical practice (target domain). As the label of target-domain images is unavailable, this cross-domain segmentation needs unsupervised domain adaptation (UDA) to make the model adapt to the target domain. However, the shape and intensity distribution of neonatal brain MRI images across the domains are largely different from adults'. Current UDA methods aim to make synthesized images similar to the target domain as a whole. But it is impossible to synthesize images with intraclass similarity because of the regional misalignment caused by the cross-domain difference. This will result in generating intraclassly incorrect intensity information from target-domain images. To address this issue, we propose an IAS-NET (joint intraclassly adaptive generative adversarial network (GAN) (IA-NET) and segmentation) framework to bridge the gap between the two domains for intraclass alignment. METHODS: Our proposed IAS-NET is an elegant learning framework that transfers the appearance of images across the domains from both image and feature perspectives. It consists of the proposed IA-NET and a segmentation network (S-NET). The proposed IA-NET is a GAN-based adaptive network that contains one generator (including two encoders and one shared decoder) and four discriminators for cross-domain transfer. The two encoders are implemented to extract original image, mean, and variance features from source and target domains. The proposed local adaptive instance normalization algorithm is used to perform intraclass feature alignment to the target domain in the feature-map level. S-NET is a U-net structure network that is used to provide semantic constraint by a segmentation loss for the training of IA-NET. Meanwhile, it offers pseudo-label images for calculating intraclass features of the target domain. Source code (in Tensorflow) is available at https://github.com/lb-whu/RAS-NET/. RESULTS: Extensive experiments are carried out on two different data sets (NeoBrainS12 and dHCP), respectively. There exist great differences in the shape, size, and intensity distribution of magnetic resonance (MR) images in the two databases. Compared to baseline, we improve the average dice score of all tissues on NeoBrains12 by 6% through adaptive training with unlabeled dHCP images. Besides, we also conduct experiments on dHCP and improved the average dice score by 4%. The quantitative analysis of the mean and variance of the synthesized images shows that the synthesized image by the proposed is closer to the target domain both in the full brain or within each class than that of the compared methods. CONCLUSIONS: In this paper, the proposed IAS-NET can improve the performance of the S-NET effectively by its intraclass feature alignment in the target domain. Compared to the current UDA methods, the synthesized images by IAS-NET are more intraclassly similar to the target domain for neonatal brain MR images. Therefore, it achieves state-of-the-art results in the compared UDA models for the segmentation task.