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Paeonia lactiflora Pall. (P. lactiflora) is an important medicinal plant in China with high ornamental value. Predicting the potential habitat of P. lactiflora is crucial for identifying its geographic distribution characteristics and ensuring its ecological and economic importance. Therefore, we aimed to predict the potential geographic distribution of P. lactiflora in China under future climate change scenarios. To this end, we used an optimized Maxent model and ArcGIS software to analyze the influence of 12 environmental variables on P. lactiflora potential distribution in China based on 291 effective distribution records. The key factors limiting the potential geographic distribution of P. lactiflora were evaluated by combining the contribution rates of the environmental variables with the significance of their replacement. The jackknife method was employed to assess the importance of these factors. Response curves were used to determine the appropriate intervals for the environmental factor variables and to analyze the changes in spatial patterns. The Maxent model exhibited a low degree of overfitting and good prediction accuracy. The main variables influencing P. lactiflora distribution were precipitation in the wettest month and hottest quarter, lowest temperature in the coldest month, and highest temperature in the warmest month. Under current climatic conditions, P. lactiflora could theoretically grow across and area of 231.1 × 104 km2 in China. Under the six future climate change scenarios, the potential geographic distribution area was reduced compared with the current distribution area, and the potentially suitable areas shifted southwestward. The majority of priority conservation sites for P. lactiflora are located in northern and northeastern China, which align with the highly favorable areas predicted by the Maxent model. The findings of this investigation can guide the selection of future introductions as well as artificial cultivation and preservation of P. lactiflora resources.

Since the 20th century, global climate has been recognized as the most important environmental factor affecting the distribution of plants. Angelica dahurica (A. dahurica) has been in great demand as a medicinal herb and flavoring, but the lack of seed sources has hindered its development. In this study, we utilized the Maxent model combined with Geographic Information System (GIS) to predict the potential habitat of A. dahurica in China based on its geographical distribution and 22 environmental factors. This prediction will serve as a valuable reference for the utilization and conservation of A. dahurica resources.The results indicated that: (1) the Maxent model exhibited high accuracy in predicting the potential habitat area of A. dahurica, with a mean value of the area under the ROC curve (AUC) at 0.879 and a TSS value above 0.6; (2) The five environmental variables with significant effects were bio6 (Min temperature of the coldest month), bio12 (Annual Precipitation), bio17 (Precipitation of Driest Quarter), elevation, and slope, contributing to a cumulative total of 89.6%. Suitable habitats for A. dahurica were identified in provinces such as Yunnan, Guizhou, Guangxi, Sichuan, Hunan, and others. The total area of suitable habitat was projected to increase, with expansion primarily in middle and high latitudes, while areas of decrease were concentrated in lower latitudes. Under future climate change scenarios, the centers of mass of suitable areas for A. dahurica were predicted to shift towards higher latitudes in the 2050s and 2090s, particularly towards the North China Plain and Northeast Plain. Overall, it holds great significance to utilize the Maxent model to predict the development and utilization of A. dahurica germplasm resources in the context of climate change.

IntroductionTilia amurensis Rupr (T. amurensis) is one endangered and national class II key protected wild plant in China. It has ornamental, material, economic, edible and medicinal values. At present, the resources of T. amurensis are decreasing, and the prediction of the distribution of its potential habitat in China can provide a theoretical basis for the cultivation and rational management of this species.MethodsIn this study, the R language was used to evaluate 358 distribution records and 38 environment variables. The MaxEnt model was used to predict the potential distribution areas of T. amurensis under the current and future climate scenarios. The dominant environmental factors affecting the distribution of T. amurensis were analyzed and the Marxan model was used to plan the priority protected areas of this species.ResultsThe results showed that Bio18, Slope, Elev, Bio1, Bio9 and Bio2 were the dominant environmental factors affecting the distribution of T. amurensis. Under the future climatic scenarios, the potential suitable areas for T. amurensis will mainly distribute in the Northeast China, the total suitable area will reduce compared with the current climate scenarios, and the general trend of the centroid of suitable habitat will be towards higher latitudes. The SPF value of the best plan obtained from the priority conservation area planning was 1.1, the BLM value was 127,616, and the priority conservation area was about 57.61×104 km2. The results suggested that climate, soil and topographic factors jointly affected the potential geographical distribution of T. amurensis, and climate and topographic factors had greater influence than soil factors.DiscussionThe total suitable area of T. amurensis in China under different climate scenarios in the future will decrease, so more effective protection should be actively adopted.

Abstract In recent decades, ecological niche models (ENMs) have been widely used to predict suitable habitats for species. However, for invasive organisms, the prediction accuracy is unclear. In this study, we employed the most widely used maximum entropy (MaxEnt) model and ensemble model (EM) Biomod2 and verified the practical effectiveness of the ENM in predicting the distribution areas of invasive insects based on the true occurrence of Hyphantria cunea in China. The results showed that when only limited data of invasive areas were used, the two ENMs could not effectively predict the distribution of suitable habitats of H. cunea, although the use of global data can greatly improve the prediction accuracy of ENMs. When analyzing the same data, Biomod2's prediction accuracy was significantly better than that of MaxEnt. For long‐term predictions, the area of suitable habitat predicted by the ENMs was much greater than the occurrence area; for short‐term predictions, the accuracy of the predicted area was significantly improved. Under the current conditions, the area of suitable habitat for H. cunea in China is 118 × 104 km2, of which 59.32% is moderately or highly suitable habitat. Future climate change could significantly increase the suitable habitat area of H. cunea in China, and the predicted area of suitable habitats in all climate scenarios exceeded 355 × 104 km2, accounting for 36.98% of the total land area in China. This study demonstrates the use of ENMs to study invasive insects and provides a reference for the management of H. cunea in China.

Chionanthus retusus (C. retusus) has a high economic and medicinal value, but in recent years it has been included in the list of China's major protected plants and China's Red List of Biodiversity due to the serious destruction of its wild germplasm resources. Based on 131 sample points of C. retusus, this study simulated potential habitats and spatial changes of C. retusus in the 21st century using the Maxent model combined with the geographic information system ArcGIS, predicted prioritized protected areas by the Marxan model, and assessed current conservation status through GAP analysis. The results showed that (1) when the regularization multiplier was 1.5 and the feature combinations were linear, quadratic, and fragmented, the area under the curve of the subjects in the training and test sets were both above 0.9, the true skill statistic value was 0.80, and the maximum Kappa value was 0.62, meaning that the model had high accuracy; (2) Temperature seasonality, annual precipitation, min temperature for coldest month, and precipitation of wettest month had relatively strong influences on species' ranges. (3) The moderately and optimally suitable habitats of C. retusus were primly located in the areas of southwestern Shanxi, central Hebei, western Henan, Shandong, Shaanxi, Anhui and Hubei; (4) Under different future climate scenarios, the area of each class of suitable habitat will increase for varied amounts compared to the current period, with a general trend of expansion to the south; (5) The C. retusus priority protected areas were mainly located in most of Shandong, southern Liaoning, southwestern Shanxi, western Henan, and central Hebei, and its conservation vacancy area was relatively large compared to its protected area. These results will provide scientific strategies for implementing long-term conservation of C. retusus in China and similar regions under warming conditions in the 21st century.

Ferritin, as an iron storage protein, has been considered to be a well-utilized iron supplement. However, the typical thermal processing of food rich in ferritin may affect the structure and function of ferritin as an iron supplement. Here, a plant ferritin (soybean seed ferritin, SSF) and an animal ferritin (donkey spleen ferritin, DSF) were used to analyze the changes in fundamental structure and iron content after thermal treatments (68 °C for 30 min, 100 °C for 10 min). Then, SSF and DSF after thermal treatment were administered intragastrically to mice to further evaluate its digestive stability and absorptivity after thermal processing. Results showed the secondary structure, oligomeric states, iron content, and digestive stability of DSF were maintained better than that of SSF after thermal treatments, indicating that DSF has a higher thermostability than SSF. Both SSF and DSF after thermal treatment exhibited higher absorptivity than untreated ferritins. SSF showed higher absorptivity than DSF whether heated or not.

Abstract Protein lattices that shift the structure and shape anisotropy in response to environmental cues are closely coupled to potential functionality. However, to design and construct shape‐anisotropic protein arrays from the same building blocks in response to different external stimuli remains challenging. Here, by a combination of the multiple, symmetric interaction sites on the outer surface of protein nanocages and the tunable features of phenylalanine‐phenylalanine interactions, a protein engineering approach is reported to construct a variety of superstructures with shape anisotropy, including 3D cubic, 2D hexagonal layered, and 1D rod‐like crystalline protein nanocage arrays by using one single protein building block. Notably, the assembly of these crystalline protein arrays is reversible, which can be tuned by external stimuli (pH and ionic strength). The anisotropic morphologies of the fabricated macroscopic crystals can be correlated with the Å‐to‐nm scale protein arrangement details by crystallographic elucidation. These results enhance the understanding of the freedom offered by an object's symmetry and inter‐object π−π stacking interactions for protein building blocks to assemble into direction‐ and shape‐anisotropic biomaterials.

Parkinson's disease (PD) is the second most common neurodegenerative disease, and communication between the gut and brain (the gut-brain axis) has been found to be essential in behavior and cognitive function. However, the exact mechanisms underlying microbiota dysbiosis in PD progression have not yet been elucidated. Our study aimed to investigate the correlation between gut microbiota disturbances and feces metabolic disorders in PD. We used 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) to induce PD models and observed mice's motor symptoms, dopaminergic (DA) neuron death, and gastrointestinal dysfunction. To identify alterations in microbiota and metabolome, feces were collected from mice and analyzed using 16 S ribosomal RNA sequencing feces metabolomics. Pearson analysis was utilized to investigate correlations between the abundances of gut microbiota components and the levels of gut microbiota metabolites, displaying their interaction networks. Our findings revealed a significant increase in Desulfobacterota in the PD mouse model and 151 differentially expressed fecal metabolites between PD and vehicle mice. Moreover, Pearson correlation analysis suggested that the protective factor N-acetyl-L-leucine (NALL) may be associated with neuroinflammation in the striatum and substantia nigra, which also had a negative relationship with the concentration of Desulfobacterota. Additionally, we found that oral administration of NALL alleviated MPTP-induced Motor Impairments and DA neuronal deficits. All in all, we concluded that the decrease of NALL might lead to a significant increase of Desulfobacterota in the MPTP model mouse and subsequently result in the damage of DA neurons via the gut-brain aix pathway.

Abstract Leonurus japonicus Houtt. is a traditional Chinese medicinal plant with high medicinal and edible value. Wild L. japonicus resources have reduced dramatically in recent years. This study predicted the response of distribution range of L. japonicus to climate change in China, which provided scientific basis for the conservation and utilization. In this study, 489 occurrence points of L. japonicus were selected based on GIS technology and spThin package. The default parameters of MaxEnt model were adjusted by using ENMeva1 package of R environment, and the optimized MaxEnt model was used to analyze the distribution of L. japonicus. When the feature combination in the model parameters is hing and the regularization multiplier is 1.5, the MaxEnt model has a higher degree of optimization. With the AUC of 0.830, our model showed a good predictive performance. The results showed that L. japonicus were widely distributed in the current period. The maximum temperature of warmest month, the min temperature of coldest month, the precipitation of wettest month, the precipitation of driest month, and altitude were the main environmental factors affecting the distribution of L. japonicus. Under the three climate change scenarios, the suitable distribution area of L. japonicus will range shift to high latitudes, indicating that the distribution of L. japonicus has a strong response to climate change. The regional change rate is the lowest under the SSP126‐2090s scenario and the highest under the SSP585‐2090s scenario.

Agastache rugosa (Fisch. & C. A. Mey.) Kuntze has been widely studied because of its high medicinal and edible value. Establishing the priority protected area of wild A. rugosa can provide scientific basis for the protection of germplasm resources. In this study, we predicted the potential suitability distribution area of A. rugosa under the current and future climate scenarios with the MaxEnt model, and the dominant climate factors affecting the distribution of A. rugosa were analyzed. Based on the above results, we predicted the priority protected areas of A. rugosa with the Marxan model. The results showed that A. rugosa is mainly distributed in the eastern and central regions of China at present. In future, the suitable area of A. rugosa will increase, otherwise a few areas will shrink back and migrate to the high latitude areas as a whole. Hydrothermal conditions are the main environmental factors affecting the distribution of A. rugosa. The priority protected areas of A. rugosa are mainly distributed in Chongqing, eastern Sichuan, southern Guizhou, western Hunan and Hubei and southwestern Shaanxi, which are basically consistent with the highly suitable areas predicted by Maxent model. The results of this study are of great significance for the protection and rational utilization of species of Agastache.

Larix gmelinii (Rupr.) Kuzen is a major tree species with high economic and ecological value in the Greater Khingan Mountains coniferous forest of Northeast China. Reconstructing the priority Conservation Area of Larix gmelinii under Climate could provide a scientific basis for its germplasm conservation and management. The present study used ensemble and Marxan model simulations to predict species distribution areas and delineate priority conservation areas for Larix gmelinii in relation to productivity characteristics, understory plant diversity characteristics, and climate change impacts. The study revealed that the Greater Khingan Mountains and the Xiaoxing'an Mountains, with an area of approximately 300 974.2 km2, were the most suitable for L. gmelinii. The stand productivity of L. gmelinii in the most suitable area was significantly higher than that in the less suitable and marginally suitable areas, but understory plant diversity was not dominant. The increase in temperature under future climate change scenarios will reduce the potential distribution and area under L. gmelinii; the species will migrate to higher latitudes of the Greater Khingan Mountains, while the degree of niche migration will gradually increase. Under the 2090s-SSP585 climate scenario, the most suitable area for L. gmelinii will completely disappear, and the climate model niche will be completely separated. Therefore, the protected area of L. gmelinii was demarcated with a target of the productivity characteristics, understory plant diversity characteristics and climate change sensitive area, and the current key protected area was 8.38 × 104 km2. Overall, the study’s findings will lay a foundation for the protection and rational development and utilization of cold temperate coniferous forests dominated by L. gmelinii in the northern forested region of the Greater Khingan Mountains.

The structure and assembly properties of ferritin derived from aquatic products remain to be explored. Constructing diverse three-dimensional (3D) protein architectures with the same building blocks has important implications for nutrient delivery, medicine and materials science. Herein, ferritin from Asterias forbesii (AfFer) was prepared, and its crystal structure was resolved at 1.91 Å for the first time. Notably, different from the crystal structure of other reported ferritin, AfFer exhibited a BCT lattice arrangement in its crystals. Bioinspired by the crystal structure of AfFer, we described an effective approach for manufacturing 3D porous, crystalline nanoarchitectures by redesigning the shared protein interface involved in different 3D protein arrays. Based on this strategy, two 3D superlattices of body-centered tetragonal and simple cubicwere constructed with ferritin molecules as the building blocks. This study provided a potentially generalizable strategy for constructing different 3D protein-based crystalline biomaterials with the same building blocks.

Upon hydrating and mixing wheat flour, wheat protein forms a network that strongly affects the structure and physicochemical properties of dough, thus affecting the properties of noodles. Different approaches have been taken to alter the gluten network structure in order to control the dough properties. In the current review, we summarize the structure and function of wheat protein, including glutenin and gliadin, and describe food components that may affect noodle quality by interacting with wheat protein. In fact, the ratio of glutenin to gliadin is closely related to the viscosity of dough, and disulfide bonds also contribute to the gluten network formation. Meanwhile, wheat protein coexists with starch and sugar in wheat dough, and thus the nature of starch may highly influence gluten formation as well. Salts, alkali, enzymes and powdered plant food can be added during dough processing to regulate the extensional properties of wheat noodles, obtaining noodles of high quality, with improved sensory and storage properties. This review describes specific methods to reinforce the wheat protein network and provides a reference for improving noodle quality.

BACKGROUND: Lycopene (LYC), a carotenoid found in abundance in ripe red fruits, exhibits higher singlet oxygen quenching activity than other carotenoids. However, the stability of LYC is extremely poor due to its high double-bond content. In this paper, a nano-encapsulation strategy based on highly stable marine-derived ferritin GF1 nanocages was used to improve the thermal stability and oxidation resistance of LYC, thereby boosting its functional effectiveness and industrial applicability. RESULTS: The preparation of GF1-LYC nanoparticles benefited from the pH-responsive reversible self-assembly of GF1 to capture LYC molecules into GF1 cavities with a LYC-to-protein ratio of 51 to 1. After the encapsulation of the LYC, the reassembled GF1 nanocages maintained intact morphology and good monodispersity. The GF1-LYC nanoparticles incorporated the characteristic LYC peaks in spectrograms, and their powder form contained the crystalline form of LYC. Molecular docking revealed that LYC bound with the inner triple-axis channel areas of GF1, interacting with VAL139, LYS72, LYS65, TYR69, PHE129, HIS133, HIS62, and TYR134 amino acids through hydrophobic bonds. Fourier transform infrared spectroscopy also demonstrated the bonding of GF1 and LYC. In comparison with free LYC, GF1 reduced the thermal degradation of encapsulated LYC at 37 °C significantly and maintained the 2,2-Diphenyl-1-picrylhydrazyl (DPPH)-scavenging ability of LYC. CONCLUSION: As expected, the water solubility, thermal stability, and antioxidant capacity of encapsulated LYC from GF1-LYC nanoparticles was notably improved in comparison with free LYC, indicating that the shell-like marine ferritin nanoplatform might enhance the stable delivery of LYC and promote its utilization in the field of food nutrition and in other industries. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]