Enterococcus casseliflavus regulates amino acid metabolism in edible insect Clanis bilineata tsingtauica: a functional metagenomics study.

Introduction: The soybean hawkmoth, Clanis bilineata tsingtauica, is an edible insect that possesses high nutritional, medicinal and economic value. It has developed into a characteristic agricultural industry in China. Methods: The dominant gut bacterium in diapause larvae of soybean hawkmoths was identified by metagenomics, and the effect of diapause time on gut microbiome composition, diversity and function was investigated. Results: Enterococcus and Enterobacter were measured to be the dominant genera, with Enterococcus casseliflavus and Enterococcus pernyi being the dominant species. Compared to the controls, the relative abundance of E. casseliflavus and E. pernyi on day 14 was lower by 54.51 and 42.45%, respectively. However, the species richness (including the index of Chao and ACE) of gut microbiota increased on day 28 compared to controls. The gene function was mainly focused on carbohydrate and amino acid metabolism. Metabolic pathways annotated for amino acids on day 14 increased by 9.83% compared to controls. It is speculated that diapause soybean hawkmoths may up-regulate amino acid metabolism by reducing E. casseliflavus abundance to maintain their nutritional balance. Additionally, tetracycline, chloromycetin and ampicillin were screened as the top three antibiotics against E. casseliflavus. Discussion: This study not only extends our knowledge of gut microbiome in soybean hawkmoths at the species level, but also provides an initial investigation of gene functionality in interaction with insect hosts.

Molecular modification and biotechnological applications of microbial aspartic proteases.

The growing preference for incorporating microbial aspartic proteases in industries is due to their high catalytic function and high degree of substrate selectivity. These properties, however, are attributable to molecular alterations in their structure and a variety of other characteristics. Molecular tools, functional genomics, and genome editing technologies coupled with other biotechnological approaches have aided in improving the potential of industrially important microbial proteases by addressing some of their major limitations, such as: low catalytic efficiency, low conversion rates, low thermostability, and less enzyme yield. However, the native folding within their full domain is dependent on a surrounding structure which challenges their functionality in substrate conversion, mainly due to their mutual interactions in the context of complex systems. Hence, manipulating their structure and controlling their expression systems could potentially produce enzymes with high selectivity and catalytic functions. The proteins produced by microbial aspartic proteases are industrially capable and far-reaching in regulating certain harmful distinctive industrial processes and the benefits of being eco-friendly. This review provides: an update on current trends and gaps in microbial protease biotechnology, exploring the relevant recombinant strategies and molecular technologies widely used in expression platforms for engineering microbial aspartic proteases, as well as their potential industrial and biotechnological applications.

Detection of citrus diseases in complex backgrounds based on image-text multimodal fusion and knowledge assistance.

Diseases pose a significant threat to the citrus industry, and the accurate detection of these diseases represent key factors for their early diagnosis and precise control. Existing diagnostic methods primarily rely on image models trained on vast datasets and limited their applicability due to singular backgrounds. To devise a more accurate, robust, and versatile model for citrus disease classification, this study focused on data diversity, knowledge assistance, and modal fusion. Leaves from healthy plants and plants infected with 10 prevalent diseases (citrus greening, citrus canker, anthracnose, scab, greasy spot, melanose, sooty mold, nitrogen deficiency, magnesium deficiency, and iron deficiency) were used as materials. Initially, three datasets with white, natural, and mixed backgrounds were constructed to analyze their effects on the training accuracy, test generalization ability, and classification balance. This diversification of data significantly improved the model's adaptability to natural settings. Subsequently, by leveraging agricultural domain knowledge, a structured citrus disease features glossary was developed to enhance the efficiency of data preparation and the credibility of identification results. To address the underutilization of multimodal data in existing models, this study explored semantic embedding methods for disease images and structured descriptive texts. Convolutional networks with different depths (VGG16, ResNet50, MobileNetV2, and ShuffleNetV2) were used to extract the visual features of leaves. Concurrently, TextCNN and fastText were used to extract textual features and semantic relationships. By integrating the complementary nature of the image and text information, a joint learning model for citrus disease features was achieved. ShuffleNetV2 + TextCNN, the optimal multimodal model, achieved a classification accuracy of 98.33% on the mixed dataset, which represented improvements of 9.78% and 21.11% over the single-image and single-text models, respectively. This model also exhibited faster convergence, superior classification balance, and enhanced generalization capability, compared with the other methods. The image-text multimodal feature fusion network proposed in this study, which integrates text and image features with domain knowledge, can identify and classify citrus diseases in scenarios with limited samples and multiple background noise. The proposed model provides a more reliable decision-making basis for the precise application of biological and chemical control strategies for citrus production.

The Key Role of Fatty Acid Synthase in Lipid Metabolism and Metamorphic Development in a Destructive Insect Pest, Spodoptera litura (Lepidoptera: Noctuidae).

Fatty acid synthase (FAS) is a key enzyme in the lipid synthesis pathway, however, its roles in insects remain largely unknown. Here, we firstly identified two FAS genes from the transcriptome dataset of the general cutworm Spodoptera litura, which is a destructive insect pest of many crops. Both SlFAS1 and SlFAS2 were highly expressed in third instar larvae and in their fat bodies. Then, we successfully silenced SlFAS1 in third instar larvae and the content of α-linolenic acid and triglyceride was significantly decreased. Besides that, the effect of FAS on the metamorphic development in S. litura was evaluated. The results indicate that after silencing SlFAS1, the survival rates of S. litura larvae decreased significantly compared to the control groups. Silencing SlFAS1 in fifth instar larvae resulted in more malformed pupae and adults, and the emergence rates were significantly reduced. Furthermore, the ecdysone content in the haemolymph of fifth instar larvae was significantly decreased after silencing SlFAS1. In addition, knocking down SlFAS1 significantly alters the expression of other key genes in the lipogenesis pathway, implying that FAS has an impact on the lipogenesis pathway. The present study deepens the understanding of FAS in insects and provides novel potential targets for managing insect pests.

Microfluidic preparation of a novel phoxim nanoemulsion pesticide against Spodoptera litura.

With continuous development of pesticide dosage forms, emulsifiable concentrates using large amounts of organic solvents are gradually obsoleted. Nanoemulsions with high water content have been developed and the preparation processes also evolved, but these processes still exist some problems, such as poor controllability and high energy consumption. Microfluidic is a controllable nanoemulsion preparation system which mainly applied to pharmaceutical synthesis. In this study, the pesticide phoxim nanoemulsion was prepared by microfluidic technology. The optimized formulation of phoxim nanoemulsion was composed of Tween 80 and pesticide emulsifier 500 as surfactant, hexyl acetate as oil, and n-propanol as co-surfactant. Moreover, when the flow rates of water and oil in the microfluidic system were adjusted to 5 µL/min and 20 µL/min, phoxim nanoemulsion was obtained with a cloud point/boiling point of 109 °C, a particle size of 21.5 ± 0.8 nm and a potential value of - 18.7 ± 0.6 mV. Furthermore, the nanoemulsion had a rapid release effect in vitro which could be fitted by the Ritger-Peppas model. The feeding toxicity of the phoxim nanoemulsion was higher than that of commercial formulation while the contact killing effect was higher than that of the active ingredient. Therefore, pesticide dosage was reduced and the insecticidal effect was enhanced by using phoxim nanoemulsions. These results also confirm the potential of microfluidics as a green process to produce pesticide nanoemulsions.