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A significant challenge in the traditional human health risk assessment of agrochemicals is the uncertainty in quantifying the interspecies differences between animal models and humans. To work toward a more accurate and animal-free risk determination, new approaches such as physiologically based kinetic (PBK) modeling have been used to perform dosimetry extrapolation from animals to humans. However, the regulatory use and acceptance of PBK modeling is limited for chemicals that lack in vivo animal pharmacokinetic (PK) data, given the inability to evaluate models. To address these challenges, this study developed PBK models in the absence of in vivo PK data for the fungicide propiconazole, an activator of constitutive androstane receptor (CAR)/pregnane X receptor (PXR). A fit-for-purpose read-across approach was integrated with hierarchical clustering − an unsupervised machine learning algorithm, to bridge the knowledge gap. The integration allowed the incorporation of a broad spectrum of attributes for analog consideration, and enabled the analog selection in a simple, reproducible, and objective manner. The applicability was evaluated and demonstrated using penconazole (source) and three pseudo-unknown target chemicals (epoxiconazole, tebuconazole and triadimefon). Applying this machine learning-enhanced read-across approach, difenoconazole was selected as the most appropriate analog for propiconazole. A mouse PBK model was developed and evaluated for difenoconazole (source), with the mode of action of CAR/PXR activation incorporated to simulate the in vivo autoinduction of metabolism. The difenoconazole mouse model then served as a template for constructing the propiconazole mouse model. A parallelogram approach was subsequently applied to develop the propiconazole rat and human models, enabling a quantitative assessment of interspecies differences in dosimetry. This integrated approach represents a substantial advancement toward refining risk assessment of propiconazole within the framework of animal alternative safety assessment strategies.

As a key executioner of pyroptosis, Gasdermin D (GSDMD) plays a crucial role in host defense and emerges as an essential therapeutic target in the treatment of inflammatory diseases. So far, the understanding of the mechanisms that regulate the protein level of GSDMD to prevent detrimental effects and maintain homeostasis is currently limited. Here, we unveil that ubiquitin-specific peptidase 18 (USP18) works as a negative regulator of pyroptosis by targeting GSDMD for degradation and preventing excessive innate immune responses. Mechanically, USP18 recruits E3 ubiquitin ligase mind bomb homolog 2 (MIB2) to catalyze ubiquitination on GSDMD at lysine (K) 168, which acts as a recognition signal for the selective autophagic degradation of GSDMD. We further confirm the alleviating effect of USP18 on LPS-triggered inflammation in vivo. Collectively, our study demonstrates the role of USP18 in regulating GSDMD-mediated pyroptosis and reveals a previously unknown mechanism by which GSDMD protein level is rigorously controlled by selective autophagy.

Davidia involucrata Baill. 1871 (D. involucrata), as a tertiary relict plant unique to China, is a national Class I protected plant with high economic value. Oil extracted from its seeds and peels can be used for both consumption and industrial purposes. It has become a popular income-earning export tree in China because of its graceful posture and beautiful white bracts. Climate change affects the distribution of the species’ potential habitat areas. Thus, studying its natural distribution pattern and future potential habitat distribution changes has great significance for the sustainable resource utilization and biodiversity conservation of D. involucrata. Here, we employed the MaxEnt model and ArcGIS software to predict the current and future (the 2050s and 2070s) potential habitats of D. involucrata via 130 species distribution records and 37 environmental variables. Meanwhile, we used the jackknife method to assess the importance of environmental factors. Our results showed the following: (1) When the RM = 4 and FC = LQHPT, the MaxEnt model exhibited the lowest complexity and overfitting degree while achieving high model prediction accuracy. The area under the curve (AUC) value of the simulated training was 0.958, indicating an excellent forecast. (2) Under the current climate scenario, D. involucrata was mainly concentrated in eastern Sichuan, western Hubei, northern Guizhou, and northwestern Hunan, with an area of 98.02 × 104 km2. (3) The precipitation in the warmest quarter (Bio18, 30%), mean temperature in the driest quarter (Bio9, 24.4%), annual mean radiation (Bio20, 14.6%), and elevation (ele, 12.7%) were the main environmental factors affecting its habitat distribution; the t contribution was 82.1%. (4) Under different future climate scenarios, the potential habitat area of D. involucrata decreased overall. Compared with the current climate scenario, the areas of potential habitats gradually decreased in both the 2050s and 2070s under the ssp126 and ssp585 climate scenarios but decreased in the 2050s and then increased in the 2070s under the ssp370 climate scenario. Therefore, it is of great significance to track and monitor the existing population or community on the basis of the possible changes in its distribution area. Moreover, the artificial breeding of its seedlings should be considered in the future to improve the quality of its germplasm resources. In summary, our findings can provide a scientific understanding of D. involucrata distribution in China and are conducive to conservation and utilization.