Pro-CRISPR PcrIIC1-associated Cas9 system for enhanced bacterial immunity.

The CRISPR system is an adaptive immune system found in prokaryotes that defends host cells against the invasion of foreign DNA1. As part of the ongoing struggle between phages and the bacterial immune system, the CRISPR system has evolved into various types, each with distinct functionalities2. Type II Cas9 is the most extensively studied of these systems and has diverse subtypes. It remains uncertain whether members of this family can evolve additional mechanisms to counter viral invasions3,4. Here we identify 2,062 complete Cas9 loci, predict the structures of their associated proteins and reveal three structural growth trajectories for type II-C Cas9. We found that novel associated genes (NAGs) tended to be present within the loci of larger II-C Cas9s. Further investigation revealed that CbCas9 from Chryseobacterium species contains a novel ß-REC2 domain, and forms a heterotetrameric complex with an NAG-encoded CRISPR-Cas-system-promoting (pro-CRISPR) protein of II-C Cas9 (PcrIIC1). The CbCas9-PcrIIC1 complex exhibits enhanced DNA binding and cleavage activity, broader compatibility for protospacer adjacent motif sequences, increased tolerance for mismatches and improved anti-phage immunity, compared with stand-alone CbCas9. Overall, our work sheds light on the diversity and 'growth evolutionary' trajectories of II-C Cas9 proteins at the structural level, and identifies many NAGs-such as PcrIIC1, which serves as a pro-CRISPR factor to enhance CRISPR-mediated immunity.

Landscape Classification System Based on RKM Clustering for Soil Survey UAV Images-Case Study of the Small Hilly Areas in Jurong City.

With the advantages of high accuracy, low cost, and flexibility, Unmanned Aerial Vehicle (UAV) images are now widely used in the fields of land survey, crop monitoring, and soil property prediction. Since the distribution of soil and landscape are closely related, this study makes use of the advantages of UAV images to classify the landscape to build a landscape classification system for soil investigation. Firstly, land use, object, and topographic factor were selected as landscape factors based on soil-forming factors. Then, based on multispectral images and Digital Elevation Models (DEM) acquired by UAV, object-oriented classification of different landscape factors was carried out. Additionally, we selected 432 sample data and validation data from the field survey. Finally, the landscape factor classification results were superimposed to obtain the landscape unit applicable to the system classification. The landscape classification system oriented to the soil survey was constructed by clustering 11,897 landscape units through the rough K-mean clustering algorithm. Compared to K-mean clustering, the rough K-mean clustering was better, with a Silhouette Coefficient of 0.26247 significantly higher than that of K-mean clustering. From the classification results, it can be found that the overall classification results are somewhat fragmented, but the landscape boundaries at the small area scale are consistent with the actual situation and the fragmented small spots are less. Comparing the small number of landscape boundaries obtained from the actual survey, we can find that the landscape boundaries in the landscape classification map are generally consistent with the actual landscape boundaries. In addition, through the analysis of two soil profile data within a landscape category, we found that the identified soil type of soil formation conditions and the landscape factor type of the landscape category is approximately the same. Therefore, this landscape classification system can be effectively used for soil surveys, and this landscape classification system is important for soil surveys to carry out the selection of survey routes, the setting of profile points, and the determination of soil boundaries.

Digital Mapping of Soil Organic Carbon Based on Machine Learning and Regression Kriging.

In the last two decades, machine learning (ML) methods have been widely used in digital soil mapping (DSM), but the regression kriging (RK) model which combines the advantages of the ML and kriging methods has rarely been used in DSM. In addition, due to the limitation of a single-model structure, many ML methods have poor prediction accuracy in undulating terrain areas. In this study, we collected the SOC content of 115 soil samples in a hilly farming area with continuous undulating terrain. According to the theory of soil-forming factors in pedogenesis, we selected 10 topographic indices, 7 vegetation indices, and 2 soil indices as environmental covariates, and according to the law of geographical similarity, we used ML and RK methods to mine the relationship between SOC and environmental covariates to predict the SOC content. Four ensemble models-random forest (RF), Cubist, stochastic gradient boosting (SGB), and Bayesian regularized neural networks (BRNNs)-were used to fit the trend of SOC content, and the simple kriging (SK) method was used to interpolate the residuals of the ensemble models, and then the SOC and residual were superimposed to obtain the RK prediction result. Moreover, the 115 samples were divided into calibration and validation sets at a ratio of 80%, and the tenfold cross-validation method was used to fit the optimal parameters of the model. From the results of four ensemble models: RF performed best in the calibration set (R2c = 0.834) but poorly in the validation set (R2v = 0.362); Cubist had good accuracy and stability in both the calibration and validation sets (R2c = 0.693 and R2v = 0.445); SGB performed poorly (R2c = 0.430 and R2v = 0.336); and BRNN had the lowest accuracy (R2c = 0.323 and R2v = 0.282). The results showed that the R2 of the four RK models in the validation set were 0.718, 0.674, 0.724, and 0.625, respectively. Compared with the ensemble models without superimposed residuals, the prediction accuracy was improved by 0.356, 0.229, 0.388, and 0.343, respectively. In conclusion, Cubist has high prediction accuracy and generalization ability in areas with complex topography, and the RK model can make full use of trends and spatial structural factors that are not easy to mine by ML models, which can effectively improve the prediction accuracy. This provides a reference for soil survey and digital mapping in complex terrain areas.

Global soil-derived ammonia emissions from agricultural nitrogen fertilizer application: A refinement based on regional and crop-specific emission factors.

Ammonia (NH3 ) emissions from fertilized soils to the atmosphere and the subsequent deposition to land surface exert adverse effects on biogeochemical nitrogen (N) cycling. The region- and crop-specific emission factors (EFs) of N fertilizer for NH3 are poorly developed and therefore the global estimate of soil NH3 emissions from agricultural N fertilizer application is constrained. Here we quantified the region- and crop-specific NH3 EFs of N fertilizer by compiling data from 324 worldwide manipulative studies and focused to map the global soil NH3 emissions from agricultural N fertilizer application. Globally, the NH3 EFs averaged 12.56% and 14.12% for synthetic N fertilizer and manure, respectively. Regionally, south-eastern Asia had the highest NH3 EFs of synthetic N fertilizer (19.48%) and Europe had the lowest (6%), which might have been associated with the regional discrepancy in the form and rate of N fertilizer use and management practices in agricultural production. Global agricultural NH3 emissions from the use of synthetic N fertilizer and manure in 2014 were estimated to be 12.32 and 3.79 Tg N/year, respectively. China (4.20 Tg N/year) followed by India (2.37 Tg N/year) and America (1.05 Tg N/year) together contributed to over 60% of the total global agricultural NH3 emissions from the use of synthetic N fertilizer. For crop-specific emissions, the NH3 EFs averaged 11.13%-13.95% for the three main staple crops (i.e., maize, wheat, and rice), together accounting for 72% of synthetic N fertilizer-induced NH3 emissions from croplands in the world and 70% in China. The region- and crop-specific NH3 EFs of N fertilizer established in this study offer references to update the default EF in the IPCC Tier 1 guideline. This work also provides an insight into the spatial variation of soil-derived NH3 emissions from the use of synthetic N fertilizer in agriculture at the global and regional scales.

Increased soil release of greenhouse gases shrinks terrestrial carbon uptake enhancement under warming.

Warming can accelerate the decomposition of soil organic matter and stimulate the release of soil greenhouse gases (GHGs), but to what extent soil release of methane (CH4 ) and nitrous oxide (N2 O) may contribute to soil C loss for driving climate change under warming remains unresolved. By synthesizing 1,845 measurements from 164 peer-reviewed publications, we show that around 1.5°C (1.16-2.01°C) of experimental warming significantly stimulates soil respiration by 12.9%, N2 O emissions by 35.2%, CH4 emissions by 23.4% from rice paddies, and by 37.5% from natural wetlands. Rising temperature increases CH4 uptake of upland soils by 13.8%. Warming-enhanced emission of soil CH4 and N2 O corresponds to an overall source strength of 1.19, 1.84, and 3.12 Pg CO2 -equivalent/year under 1°C, 1.5°C, and 2°C warming scenarios, respectively, interacting with soil C loss of 1.60 Pg CO2 /year in terms of contribution to climate change. The warming-induced rise in soil CH4 and N2 O emissions (1.84 Pg CO2 -equivalent/year) could reduce mitigation potential of terrestrial net ecosystem production by 8.3% (NEP, 22.25 Pg CO2 /year) under warming. Soil respiration and CH4 release are intensified following the mean warming threshold of 1.5°C scenario, as compared to soil CH4 uptake and N2 O release with a reduced and less positive response, respectively. Soil C loss increases to a larger extent under soil warming than under canopy air warming. Warming-raised emission of soil GHG increases with the intensity of temperature rise but decreases with the extension of experimental duration. This synthesis takes the lead to quantify the ecosystem C and N cycling in response to warming and advances our capacity to predict terrestrial feedback to climate change under projected warming scenarios.

Differences in the responses of flow and nutrient load to isolated and coupled future climate and land use changes.

Understanding the differences in the responses of river hydrology and water quality to climate and land use changes is particularly crucial for the development and management of water resources in the future. This study was carried out to assess the isolated and coupled effects of future climate change and land use change on the flow and nutrient load of the Xitiaoxi watershed in southeast China by applying the calibrated Hydrological Simulation Program Fortran model. Four representative concentration pathways released by the Intergovernmental Panel on Climate Change and two projected land use change scenarios were used to simulate future conditions. The results indicate that climate change would result in flow increased with an average variation of 25.2% in the future, and the increased flow would be mainly concentrated on the high flow part of the total flow duration curve. Climate change would also induce seasonal shifts to nutrient load. The effects of land use change showed that nutrient load was more sensitive than flow, made Orthophosphate load increase by 2.8%-154.7%, and flow increase by 7.2%-15.1%. The results for coupled climate and land use changes indicate that flow and nutrient load would be more affected by climate change than by land use change. Climate and land use changes may amplify or weaken each other's effects on flow and nutrient load, which suggests that both should be incorporated into hydrologic models when studying the future conditions. The results of this study can help decision-makers guide management practices that aim to minimize flow and nutrient load.

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification.

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively.

A meta-analysis of fertilizer-induced soil NO and combined NO+N2 O emissions.

Soils are among the important sources of atmospheric nitric oxide (NO) and nitrous oxide (N2 O), acting as a critical role in atmospheric chemistry. Updated data derived from 114 peer-reviewed publications with 520 field measurements were synthesized using meta-analysis procedure to examine the N fertilizer-induced soil NO and the combined NO+N2 O emissions across global soils. Besides factors identified in earlier reviews, additional factors responsible for NO fluxes were fertilizer type, soil C/N ratio, crop residue incorporation, tillage, atmospheric carbon dioxide concentration, drought and biomass burning. When averaged across all measurements, soil NO-N fluxes were estimated to be 4.06 kg ha-1  yr-1 , with the greatest (9.75 kg ha-1  yr-1 ) in vegetable croplands and the lowest (0.11 kg ha-1  yr-1 ) in rice paddies. Soil NO emissions were more enhanced by synthetic N fertilizer (+38%), relative to organic (+20%) or mixed N (+18%) sources. Compared with synthetic N fertilizer alone, synthetic N fertilizer combined with nitrification inhibitors substantially reduced soil NO emissions by 81%. The global mean direct emission factors of N fertilizer for NO (EFNO ) and combined NO+N2 O (EFc ) were estimated to be 1.16% and 2.58%, with 95% confidence intervals of 0.71-1.61% and 1.81-3.35%, respectively. Forests had the greatest EFNO (2.39%). Within the croplands, the EFNO (1.71%) and EFc (4.13%) were the greatest in vegetable cropping fields. Among different chemical N fertilizer varieties, ammonium nitrate had the greatest EFNO (2.93%) and EFc (5.97%). Some options such as organic instead of synthetic N fertilizer, decreasing N fertilizer input rate, nitrification inhibitor and low irrigation frequency could be adopted to mitigate soil NO emissions. More field measurements over multiyears are highly needed to minimize the estimate uncertainties and mitigate soil NO emissions, particularly in forests and vegetable croplands.

Methane and Nitrous Oxide Emissions Reduced Following Conversion of Rice Paddies to Inland Crab-Fish Aquaculture in Southeast China.

Aquaculture is an important source of atmospheric methane (CH4) and nitrous oxide (N2O), while few direct flux measurements are available for their regional and global source strength estimates. A parallel field experiment was performed to measure annual CH4 and N2O fluxes from rice paddies and rice paddy-converted inland crab-fish aquaculture wetlands in southeast China. Besides N2O fluxes dependent on water/sediment mineral N and CH4 fluxes related to water chemical oxygen demand, both CH4 and N2O fluxes from aquaculture were related to water/sediment temperature, sediment dissolved organic carbon, and water dissolved oxygen concentration. Annual CH4 and N2O fluxes from inland aquaculture averaged 0.37 mg m(-2) h(-1) and 48.1 µg m(-2) h(-1), yielding 32.57 kg ha(-1) and 2.69 kg N2O-N ha(-1), respectively. The conversion of rice paddies to aquaculture significantly reduced CH4 and N2O emissions by 48% and 56%, respectively. The emission factor for N2O was estimated to be 0.66% of total N input in the feed or 1.64 g N2O-N kg(-1) aquaculture production in aquaculture. The conversion of rice paddies to inland aquaculture would benefit for reconciling greenhouse gas mitigation and agricultural income increase as far as global warming potentials and net ecosystem economic profits are of concomitant concern. Some agricultural practices such as better aeration and feeding, and fallow season dredging would help to lower CH4 and N2O emissions from inland aquaculture. More field measurements from inland aquaculture are highly needed to gain an insight into national and global accounting of CH4 and N2O emissions.

Engineering a sensitive visual-tracking reporter system for real-time monitoring phosphorus deficiency in tobacco.

Plant phosphorus (P) diagnosis is widely used for monitoring P status and guiding P fertilizer application in field conditions. The common methods for predicting plant response to P are time- and labour-consuming chemical measurements of the extractable soil P and plant P concentrations. In this study, we successfully generated a visual reporter system in tobacco (Nicotiana tabacum L.) to monitor plant P status by expressing of a Purple gene (Pr) isolated from cauliflower (Brassica oleracea var botrytis) driven by the promoter (Pro) of OsPT6, a P-starvation-induced rice gene. The leaves of OsPT6pro::Pr (PT6pro::Pr) transgenic tobacco continuously turned into dark purple with the increase of duration and severity of P deficiency, and recovered rapidly to basal green colour upon resupply of P. The expression of several anthocyanin biosynthesis involving genes was strongly activated in the transgenic tobacco in comparison to wild type under P-deficient condition. Such additive purple colour was not detected by deficiencies of other major- and micronutrients or stresses of salt, drought and cold. There was an extremely high correlation between P concentration and anthocyanin accumulation in the transgenic tobacco leaves. Using a hyperspectral sensing technology, P concentration in the leaves of transgenic plants could be predicted by the reflectance spectra at 554 nm wavelength with approximately 0.16 as the threshold value of the P deficiency. Taken together, the colour-based visual reporter system could be specifically and readily used for monitoring the plant P status by naked eyes and accurately assessed by spectral reflectance.

Inferring land use and land cover impact on stream water quality using a Bayesian hierarchical modeling approach in the Xitiaoxi River Watershed, China.

Lake eutrophication has become a very serious environmental problem in China. If water pollution is to be controlled and ultimately eliminated, it is essential to understand how human activities affect surface water quality. A recently developed technique using the Bayesian hierarchical linear regression model revealed the effects of land use and land cover (LULC) on stream water quality at a watershed scale. Six LULC categories combined with watershed characteristics, including size, slope, and permeability were the variables that were studied. The pollutants of concern were nutrient concentrations of total nitrogen (TN) and total phosphorus (TP), common pollutants found in eutrophication. The monthly monitoring data at 41 sites in the Xitiaoxi Watershed, China during 2009-2010 were used for model demonstration. The results showed that the relationships between LULC and stream water quality are so complicated that the effects are varied over large areas. The models suggested that urban and agricultural land are important sources of TN and TP concentrations, while rural residential land is one of the major sources of TN. Certain agricultural practices (excessive fertilizer application) result in greater concentrations of nutrients in paddy fields, artificial grasslands, and artificial woodlands. This study suggests that Bayesian hierarchical modeling is a powerful tool for examining the complicated relationships between land use and water quality on different scales, and for developing land use and water management policies.

Pathogenesis of gout: Exploring more therapeutic target.

Gout is a chronic metabolic and immune disease, and its specific pathogenesis is still unclear. When the serum uric acid exceeds its saturation in the blood or tissue fluid, it is converted to monosodium urate crystals, which lead to acute arthritis of varying degrees, urinary stones, or irreversible peripheral joint damage, and in severe cases, impairment of vital organ function. Gout flare is a clinically significant state of acute inflammation in gout. The current treatment is mostly anti-inflammatory analgesics, which have numerous side effects with limited treatment methods. Gout pathogenesis involves many aspects. Therefore, exploring gout pathogenesis from multiple perspectives is conducive to identifying more therapeutic targets and providing safer and more effective alternative treatment options for patients with gout flare. Thus, this article is of great significance for further exploring the pathogenesis of gout. The author summarizes the pathogenesis of gout from four aspects: signaling pathways, inflammatory factors, intestinal flora, and programmed cell death, focusing on exploring more new therapeutic targets.

Models of gouty nephropathy: exploring disease mechanisms and identifying potential therapeutic targets.

Gouty nephropathy (GN) is a metabolic disease with persistently elevated blood uric acid levels. The main manifestations of GN are crystalline kidney stones, chronic interstitial nephritis, and renal fibrosis. Understanding the mechanism of the occurrence and development of GN is crucial to the development of new drugs for prevention and treatment of GN. Currently, most studies exploring the pathogenesis of GN are primarily based on animal and cell models. Numerous studies have shown that inflammation, oxidative stress, and programmed cell death mediated by uric acid and sodium urate are involved in the pathogenesis of GN. In this article, we first review the mechanisms underlying the abnormal intrinsic immune activation and programmed cell death in GN and then describe the characteristics and methods used to develop animal and cell models of GN caused by elevated uric acid and deposited sodium urate crystals. Finally, we propose potential animal models for GN caused by abnormally high uric acid levels, thereby provide a reference for further investigating the methods and mechanisms of GN and developing better prevention and treatment strategies.

Potential Therapeutic Mechanism of Radix Angelicae Biseratae and Dipsaci Radix Herb Pair against Osteoarthritis: Based on Network Pharmacology and Molecular Docking.

Background: A major contributor to older disability is osteoarthritis. Radix Angelicae Biseratae (known as Duhuo in China, DH, the dried rhizome of Angelica pubescens) and Dipsaci Radix (known as Xuduan in China, XD, the dried rhizome of Dipsacus asper Wall) herb pair (DXHP) is widely used to treat osteoarthritis, but the underlying molecular mechanisms still have not been revealed. This research aimed to illustrate the therapeutic mechanism of DXHP against osteoarthritis through the techniques of network pharmacology and molecular docking. Methods: Gene targets for osteoarthritis and active ingredients for DXHP were screened based on the pharmacology public database and the gene-disease target database. The software program Cytoscape was used to visualize the active chemical target-disease gene network. The STRING biological information website was used to investigate protein interactions. On the Metascape bioinformatics website, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were carried out. The molecular docking of the important chemicals and primary targets identified by the aforementioned screening was performed using Autodock software. Results: Twenty-six active substances from the DXHP that had strong connections to 138 osteoarthritis-related targets were screened out. According to network analysis, TNF, GAPDH, IL-6, AKT-1, IL-1B, and VEGFA are prospective therapeutic targets, while osthole, cauloside A, ammidin, angelicone, beta-sitosterol, and asperosaponin VI may be significant active components. 1705 biological processes (BP), 155 molecular functions (MF), and 89 cellular components (CC) were identified by GO analysis. KEGG analysis indicated that IL-17, NF-kappa B, HIF-1, MAPK, and AGE-RAGE signaling pathways are potentially involved. Molecular docking showed that cauloside A, osthole, and ß-sitosterol have excellent binding activity with main targets. Conclusions: This study comprehensively illuminated the active ingredients, potential targets, primary pharmacological effects, and relevant mechanisms of the DXHP in the treatment of OA. These findings provide fresh thoughts into the therapeutic mechanisms of the main active ingredients of DXHP and provide a reference for further exploration and clinical applications of DXHP.

An integrative analysis to predict the active compounds and explore polypharmacological mechanisms of Orthosiphon stamineus Benth.

BACKGROUND: Orthosiphon stamineus Benth is a dietary supplement and traditional Chinese herb with widespread clinical applications, but a comprehensive understanding of its active compounds and polypharmacological mechanisms is lacking. This study aimed to systematically investigate the natural compounds and molecular mechanisms of O. stamineus via network pharmacology. METHODS: Information on compounds from O. stamineus was collected via literature retrieval, while physicochemical properties and drug-likeness were evaluated using SwissADME. Protein targets were screened using SwissTargetPrediction, while the compound-target networks were constructed and analyzed via Cytoscape with CytoHubba for seed compounds and core targets. Enrichment analysis and disease ontology analysis were then carried out, generating target-function and compound-target-disease networks to intuitively explore potential pharmacological mechanisms. Lastly, the relationship between active compounds and targets was confirmed via molecular docking and dynamics simulation. RESULTS: A total of 22 key active compounds and 65 targets were identified and the main polypharmacological mechanisms of O. stamineus were addressed. The molecular docking results suggested that nearly all core compounds and their targets possess good binding affinity. In addition, the separation of receptor and ligands was not observed in all dynamics simulation processes, whereas complexes of orthosiphol Z-AR and Y-AR performed best in simulations of molecular dynamics. CONCLUSION: This study successfully identified the polypharmacological mechanisms of the main compounds in O. stamineus, and predicted five seed compounds along with 10 core targets. Moreover, orthosiphol Z, orthosiphol Y, and their derivatives can be utilized as lead compounds for further research and development. The findings here provide improved guidance for subsequent experiments, and we identified potential active compounds for drug discovery or health promotion.

Clinical efficacy of Yiqi Yangxue formula on knee osteoarthritis and unraveling therapeutic mechanism through plasma metabolites in rats.

Objective: To observe the clinical efficacy and safety of Yiqi Yangxue formula (YQYXF) on knee osteoarthritis (KOA), and to explore the underlying therapeutic mechanism of YQYXF through endogenous differential metabolites and their related metabolic pathways. Methods: A total of 61 KOA patients were recruited and divided into the treatment group (YQYXF, 30 cases) and the control group (celecoxib, Cxb, 31 cases). Effects of these two drugs on joint pain, swelling, erythrocyte sedimentation rate (ESR) and c-reactive protein (CRP) were observed, and their safety and adverse reactions were investigated. In animal experiments, 63 SD rats were randomly divided into normal control (NC) group, sham operation (sham) group, model (KOA) group, Cxb group, as well as low-dose (YL), medium-dose (YM), and high-dose groups of YQYXF (YH). The KOA rat model was established using a modified Hulth method. Ultra-high-performance liquid chromatography/Q Exactive HF-X Hybrid Quadrupole-Orbitrap Mass (UHPLC-QE-MS)-based metabolomics technology was used to analyze the changes of metabolites in plasma samples of rats. Comprehensive (VIP) >1 and t-test p < 0.05 conditions were used to screen the disease biomarkers of KOA, and the underlying mechanisms of YQYXF were explored through metabolic pathway enrichment analysis. The related markers of YQYXF were further verified by ELISA (enzyme-linked immunosorbent assay). Results: YQYXF can improve joint pain, swelling, range of motion, joint function, Michel Lequesen index of severity for osteoarthritis (ISOA) score, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score, ESR, and CRP. No apparent adverse reactions were reported. In addition, YQYXF can improve cartilage damage in KOA rats, reverse the abnormal changes of 16 different metabolites, and exert an anti-KOA effect mainly through five metabolic pathways. The levels of reactive oxygen species (ROS) and glutathione (GSH) were significantly decreased after the treatment of YQYXF. Conclusion: YQYXF can significantly improve the clinical symptoms of KOA patients without obvious adverse reactions. It mainly improved KOA through modulating lipid metabolism-related biomarkers, reducing lipid peroxidation and oxidative stress.

Polyphenols: Natural food grade biomolecules for treating neurodegenerative diseases from a multi-target perspective.

As natural functional bioactive ingredients found in foods and plants, polyphenols play various antioxidant and anti-inflammatory roles to prevent the development of disease and restore human health. The multi-target modulation of polyphenols provides a novel practical therapeutic strategy for neurodegenerative diseases that are difficult to treat with traditional drugs like glutathione and cholinesterase inhibitors. This review mainly focuses on the efficacy of polyphenols on ischemic stroke, Parkinson's disease and Alzheimer's disease, including in vivo and in vitro experimental studies. It is further emphasized that polyphenols exert neuroprotective effects primarily through inhibiting production of oxidative stress and inflammatory cytokines, which may be the underlying mechanism. However, polyphenols are still rarely used as medicines to treat neurodegenerative diseases. Due to the lack of clinical trials, the mechanism of polyphenols is still in the stage of insufficient exploration. Future large-scale multi-center randomized controlled trials and in-depth mechanism studies are still needed to fully assess the safety, efficacy and side effects of polyphenols.

The compact Casπ (Cas12l) 'bracelet' provides a unique structural platform for DNA manipulation.

CRISPR-Cas modules serve as the adaptive nucleic acid immune systems for prokaryotes, and provide versatile tools for nucleic acid manipulation in various organisms. Here, we discovered a new miniature type V system, CRISPR-Casπ (Cas12l) (~860 aa), from the environmental metagenome. Complexed with a large guide RNA (~170 nt) comprising the tracrRNA and crRNA, Casπ (Cas12l) recognizes a unique 5' C-rich PAM for DNA cleavage under a broad range of biochemical conditions, and generates gene editing in mammalian cells. Cryo-EM study reveals a 'bracelet' architecture of Casπ effector encircling the DNA target at 3.4 Å resolution, substantially different from the canonical 'two-lobe' architectures of Cas12 and Cas9 nucleases. The large guide RNA serves as a 'two-arm' scaffold for effector assembly. Our study expands the knowledge of DNA targeting mechanisms by CRISPR effectors, and offers an efficient but compact platform for DNA manipulation.

Regulatory Effects of Quercetin on Bone Homeostasis: Research Updates and Future Perspectives.

The imbalance of bone homeostasis has become a major public medical problem amid the background of an aging population, which is closely related to the occurrence of osteoporosis, osteoarthritis, and fractures. Presently, most drugs used in the clinical treatment of bone homeostasis imbalance are bisphosphonates, calcitonin, estrogen receptor modulators, and biological agents that inhibit bone resorption or parathyroid hormone analogs that promote bone formation. However, there are many adverse reactions. Therefore, it is necessary to explore potential drugs. Quercetin, as a flavonol compound with various biological activities, is widely distributed in plants. Studies have found that quercetin can regulate bone homeostasis through multiple pathways and targets. An in-depth exploration of the pharmacological mechanism of quercetin is of great significance for the development of new drugs. This review discusses the therapeutic mechanisms of quercetin on bone homeostasis, such as regulating the expression of long non-coding RNA, signaling pathways of bone metabolism, various types of programmed cell death, bone nutrients supply pathways, anti-oxidative stress, anti-inflammation, and activation of Sirtuins. We also summarize recent progress in improving quercetin bioavailability and propose some issues worth paying attention to, which may help guide future research efforts.

How to Model Rheumatoid Arthritis in Animals: From Rodents to Non-Human Primates.

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease influenced by both genetic and environmental factors. At present, rodent models are primarily used to study the pathogenesis and treatment of RA. However, the genetic divergences between rodents and humans determine differences in the development of RA, which makes it necessary to explore the establishment of new models. Compared to rodents, non-human primates (NHPs) are much more closely related to humans in terms of the immune system, metabolic conditions, and genetic make-up. NHPs model provides a powerful tool to study the development of RA and potential complications, as well as preclinical studies in drug development. This review provides a brief overview of the RA animal models, emphasizes the replication methods, pros and cons, as well as evaluates the validity of the rodent and NHPs models.