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In recent years, glucagon-like peptide-1 (GLP-1) has demonstrated considerable potential in the treatment of type 2 diabetes (T2D) and obesity. However, the half-life of naturally occurring GLP-1 is quite short in vivo. Two common strategies employed for half-life extension are the use of the Albumin-binding domain (ABD) and XTEN polypeptide, which operate through different mechanisms. In this study, we designed an innovative GLP-1 receptor agonist with an extended duration of action. This new construct incorporated an albumin binding domain (ABD) and an XTEN sequence (either XTEN144 or XTEN288) as carriers. We referred to these fusion proteins as GLP-ABD-XTEN144 and GLP-ABD-XTEN288. In an E. coli system, the said constructs were efficaciously produced in substantial quantity. It was observed from in vitro studies that the fusion protein GLP-ABD-XTEN144 demonstrated a five times stronger affinity towards human serum albumin (HSA), boasting a binding affinity (Kd) of 5.50 nM. This was in contrast to GLP-ABD-XTEN288, whose Kd value was registered at 27.78 nM. Moreover, GLP-ABD-XTEN144 presented a half-life of 12.9 h in mice, thus exceeding the corresponding value for GLP-ABD-XTEN288, 7.32 h in mice. Both these fusion proteins significantly mitigated non-fasting blood sugar levels and overall food consumption for 48 h subsequent to a one-time injection in mice. Notably, GLP-ABD-XTEN144 exhibited more pronounced hypoglycemic activity and food inhibitory effects than GLP-ABD-XTEN288. The designed GLP-ABD-XTEN144 fusion protein shows promising prospects for clinical application in T2D treatment. Our findings also suggest that ABD and XTEN polypeptides synergistically contribute to half-life extension, further enhancing the pharmacokinetic characteristics of a payload.

Bactrocera dorsalis is a notorious polyphagous pest in China, and its management strategies largely depend on methyl eugenol (ME), which has been widely used as an attractant to monitor and eradicate B. dorsalis populations for seven decades. However, the non-responsiveness levels in field B. dorsalis populations to ME is unknown. In this study, we monitored the response to ME in field populations from the four most heavily infested provinces in China, and the results showed that the populations had lower sensitivity to ME relative to GZS susceptible strain. The percent responsiveness of the lowest sensitivity population was 5.88-, 3.47-, and 1.47-fold lower relative to the susceptible strain at doses of 1, 10, and 100 µL of ME, respectively. Gene expression analysis and inhibitor assays further revealed that odorant binding protein (BdorOBP2, BdorOBP83b) and the P450 enzyme system may be associated with the lower response to ME. To our knowledge, this work is the first to report that the P450 enzyme system confers a lower responsiveness to lure insects. These findings provided valuable insights for exploiting ME non-responsiveness to protect sterile males from ME-based control strategies and the use of lures combined with insecticides.

Pest early warning technology is part of the prerequisite for the timely and effective control of pest outbreaks. Traditional pest warning system with artificial mathematical statistics, radar, and remote sensing has some deficiency in many aspects, such as higher cost, weakness of accuracy, low efficiency, and so on. In this study, Pest image data was collected and information about four major vegetable pests (Bemisia tabaci (Gennadius), Phyllotreta striolata (Fabricius), Plutella xylostella (Linnaeus), and Frankliniella occidentalis (Pergande) (Thysanoptera, Thripidae)) in southern China was extracted. A multi-sensor network system was constructed to collect small-scale environmental data on vegetable production sites. The key factors affecting the distribution of pests were discovered by multi-dimensional information, such as soil, environment, eco-climate, and meteorology of vegetable fields, and finally, the vegetable pest warning system that is based on multidimensional big data (VPWS-MBD) was implemented. Pest and environmental data from Guangzhou Dongsheng Bio-Park were collected from June 2017 to February 2018. The number of pests is classified as level I (0–56), level II (57–131), level III (132–299), and level IV (above 300) by K-Means algorithm. The Pearson correlation coefficient and the grey relational analysis algorithm were used to calculate the five key influence factors of rainfall, soil temperature, air temperature, leaf surface humidity, and soil moisture. Finally, Back Propagation (BP) Neural Network was used for classification prediction. The result shows: I-level warning accuracy was 96.14%, recall rate was 97.56%; II-level pest warning accuracy was 95.34%, the recall rate was 96.45%; III-level pest warning accuracy of 100%, the recall rate was 96.28%; IV-level pest warning accuracy of 100%, recall rate was 100%. It proves that the early warning system can effectively predict vegetable pests and achieve the early warning of vegetable pest’s requirements, with high availability.