Trifolirhizin relieves renal injury in a diabetic nephropathy model by inducing autophagy and inhibiting oxidative stress through the regulation of PI3K/AKT/mTOR pathway

Purpose: To evaluate the effects of trifolirhizin on diabetic nephropathy (DN), and the mechanism of action. Methods: Male db/db mice (8 weeks, n = 24) and age-matched control mice (n = 6) were obtained. The mice were further divided into four groups and administered increasing doses of trifolirhizin (0, 12.5, 25 and 50 mg/kg). Histological analysis of renal tissues were performed by H & E staining. Blood urea nitrogen (BUN) and creatinine were determined using enzyme-linked immunosorbent assay (ELISA). Immunoblot and TUNEL assay were performed to investigate the effect of trifolirhizin on autophagy and apoptosis, while ELISA and dihydroethidium (DHE) staining were conducted to evaluate reactive oxygen species (ROS), malondialdehyde (MDA) and superoxide dismutase (SOD) levels. The effect of trifolirhizin on PI3K/AKT/mTOR pathway was determined using Immunoblot assays. Results: Trifolirhizin alleviated renal injury in diabetic mice, and also activate autophagy and inhibited apoptosis in the renal tissues in diabetic mice (p < 0.001). In addition, trifolirhizin inhibited the oxidative stress response in the renal tissue in diabetic mice (p < 0.001). Trifolirhizin further inhibited PI3K/AKT/mTOR pathway and therefore relieved renal injury in the diabetic nephropathy model (p < 0.001). Conclusion: Trifolirhizin alleviates renal injury in diabetic mice, activates autophagy, and inhibits apoptosis in renal tissue of diabetic mice. Therefore, trifolirhizin is a promising a promising drug for the treatment of DN

A key hub for climate systems: deciphering from Southern Ocean sea surface temperature variability

The Southern Ocean connects the Pacific, Atlantic, and Indian Oceans, serving as a key hub for the global overturning circulation. The climate of the Southern Ocean is closely linked to the low-latitude equatorial Pacific, as well as the high-latitude regions of the North Atlantic, making it an important component of the global climate system. Due to the interactions of various processes such as atmospheric, oceanic, and ice cover, the Southern Ocean exhibits a complex and variable sea surface temperature structure. Satellite observations indicate that since 1980, the sea surface temperature of the Southern Ocean has been cooling, contrary to the global warming trend. However, due to the relatively short length of satellite observations, the specific mechanisms are not yet clear. Here, we used the EOF method to analyze sea surface temperature data since 1870 (HadISST1 and ERSSTV5), with three main separated modes explaining over 70% of the sea temperature variability. Among them, the first mode shows widespread positive sea surface temperature anomalies in the Southern Ocean, with a time series change consistent with global temperature anomalies, representing a mode of global warming. The second mode corresponds to the Atlantic Multidecadal Oscillation (AMO) but with a lag of approximately 4 years. The third mode is consistent with the variability of the El Niño-Southern Oscillation (ENSO). Furthermore, our study indicates that despite the ongoing global warming since 1980, the negative phase of AMO and positive phase of ENSO may counteract the effects of global warming, leading to an overall cooling trend in the sea surface temperature of the Southern Ocean

Integrating TSPO PET imaging and transcriptomics to unveil the role of neuroinflammation and amyloid-β deposition in Alzheimer's disease.

PURPOSE Despite the revealed role of immunological dysfunctions in the development and progression of Alzheimer's disease (AD) through animal and postmortem investigations, direct evidence regarding the impact of genetic factors on microglia response and amyloid-β (Aβ) deposition in AD individuals is lacking. This study aims to elucidate this mechanism by integrating transcriptomics and TSPO, Aβ PET imaging in clinical AD cohort. METHODS We analyzed 85 patients with PET/MR imaging for microglial activation (TSPO, [18F]DPA-714) and Aβ ([18F]AV-45) within the prospective Alzheimer's Disease Immunization and Microbiota Initiative Study Cohort (ADIMIC). Immune-related differentially expressed genes (IREDGs), identified based on AlzData, were screened and verified using blood samples from ADIMIC. Correlation and mediation analyses were applied to investigate the relationships between immune-related genes expression, TSPO and Aβ PET imaging. RESULTS TSPO uptake increased significantly both in aMCI (P < 0.05) and AD participants (P < 0.01) and showed a positive correlation with Aβ deposition (r = 0.42, P < 0.001). Decreased expression of TGFBR3, FABP3, CXCR4 and CD200 was observed in AD group. CD200 expression was significantly negatively associated with TSPO PET uptake (r =-0.33, P = 0.013). Mediation analysis indicated that CD200 acted as a significant mediator between TSPO uptake and Aβ deposition (total effect B = 1.92, P = 0.004) and MMSE score (total effect B =-54.01, P = 0.003). CONCLUSION By integrating transcriptomics and TSPO PET imaging in the same clinical AD cohort, this study revealed CD200 played an important role in regulating neuroinflammation, Aβ deposition and cognitive dysfunction

Unveiling macrophage diversity in myocardial ischemia-reperfusion injury: identification of a distinct lipid-associated macrophage subset

Background and objectiveMacrophages play a crucial and dichotomous role cardiac repair following myocardial ischemia-reperfusion, as they can both facilitate tissue healing and contribute to injury. This duality is intricately linked to environmental factors, and the identification of macrophage subtypes within the context of myocardial ischemia-reperfusion injury (MIRI) may offer insights for the development of more precise intervention strategies.MethodsSpecific marker genes were used to identify macrophage subtypes in GSE227088 (mouse single-cell RNA sequencing dataset). Genome Set Enrichment Analysis (GSEA) was further employed to validate the identified LAM subtypes. Trajectory analysis and single-cell regulatory network inference were executed using the R packages Monocle2 and SCENIC, respectively. The conservation of LAM was verified using human ischemic cardiomyopathy heart failure samples from the GSE145154 (human single-cell RNA sequencing dataset). Fluorescent homologous double-labeling experiments were performed to determine the spatial localization of LAM-tagged gene expression in the MIRI mouse model.ResultsIn this study, single-cell RNA sequencing (scRNA-seq) was employed to investigate the cellular landscape in ischemia-reperfusion injury (IRI). Macrophage subtypes, including a novel Lipid-Associated Macrophage (LAM) subtype characterized by high expression of Spp1, Trem2, and other genes, were identified. Enrichment and Progeny pathway analyses highlighted the distinctive functional role of the SPP1+ LAM subtype, particularly in lipid metabolism and the regulation of the MAPK pathway. Pseudotime analysis revealed the dynamic differentiation of macrophage subtypes during IRI, with the activation of pro-inflammatory pathways in specific clusters. Transcription factor analysis using SCENIC identified key regulators associated with macrophage differentiation. Furthermore, validation in human samples confirmed the presence of SPP1+ LAM. Co-staining experiments provided definitive evidence of LAM marker expression in the infarct zone. These findings shed light on the role of LAM in IRI and its potential as a therapeutic target.ConclusionIn conclusion, the study identifies SPP1+ LAM macrophages in ischemia-reperfusion injury and highlights their potential in cardiac remodeling

Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment

We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70–75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70–80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands.Cancer Research UK, Grant/Award Number: FC001003; Changzhou Science and Technology Bureau, Grant/Award Number: CE20200503; Department of Energy and Climate Change, Grant/Award Numbers: DE-AR001213, DE-SC0020400, DE-SC0021303; H2020 European Institute of Innovation and Technology, Grant/Award Numbers: 675728, 777536, 823830; Institut national de recherche en informatique et en automatique (INRIA), Grant/Award Number: Cordi-S; Lietuvos Mokslo Taryba, Grant/Award Numbers: S-MIP-17-60, S-MIP-21-35; Medical Research Council, Grant/Award Number: FC001003; Japan Society for the Promotion of Science KAKENHI, Grant/Award Number: JP19J00950; Ministerio de Ciencia e Innovación, Grant/Award Number: PID2019-110167RB-I00; Narodowe Centrum Nauki, Grant/Award Numbers: UMO-2017/25/B/ST4/01026, UMO-2017/26/M/ST4/00044, UMO-2017/27/B/ST4/00926; National Institute of General Medical Sciences, Grant/Award Numbers: R21GM127952, R35GM118078, RM1135136, T32GM132024; National Institutes of Health, Grant/Award Numbers: R01GM074255, R01GM078221, R01GM093123, R01GM109980, R01GM133840, R01GN123055, R01HL142301, R35GM124952, R35GM136409; National Natural Science Foundation of China, Grant/Award Number: 81603152; National Science Foundation, Grant/Award Numbers: AF1645512, CCF1943008, CMMI1825941, DBI1759277, DBI1759934, DBI1917263, DBI20036350, IIS1763246, MCB1925643; NWO, Grant/Award Number: TOP-PUNT 718.015.001; Wellcome Trust, Grant/Award Number: FC00100

Impact of AlphaFold on Structure Prediction of Protein Complexes: The CASP15-CAPRI Experiment

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homo-dimers, 3 homo-trimers, 13 hetero-dimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their 5 best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% for the targets compared to 8% two years earlier, a remarkable improvement resulting from the wide use of the AlphaFold2 and AlphaFold-Multimer software. Creative use was made of the deep learning inference engines affording the sampling of a much larger number of models and enriching the multiple sequence alignments with sequences from various sources. Wide use was also made of the AlphaFold confidence metrics to rank models, permitting top performing groups to exceed the results of the public AlphaFold-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem

Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem

Numerical Study on an RBF-FD Tangent Plane Based Method for Convection&ndash;Diffusion Equations on Anisotropic Evolving Surfaces

In this paper, we present a fully Lagrangian method based on the radial basis function (RBF) finite difference (FD) method for solving convection&ndash;diffusion partial differential equations (PDEs) on evolving surfaces. Surface differential operators are discretized by the tangent plane approach using Gaussian RBFs augmented with two-dimensional (2D) polynomials. The main advantage of our method is the simplicity of calculating differentiation weights. Additionally, we couple the method with anisotropic RBFs (ARBFs) to obtain more accurate numerical solutions for the anisotropic growth of surfaces. In the ARBF interpolation, the Euclidean distance is replaced with a suitable metric that matches the anisotropic surface geometry. Therefore, it will lead to a good result on the aspects of stability and accuracy of the RBF-FD method for this type of problem. The performance of this method is shown for various convection&ndash;diffusion equations on evolving surfaces, which include the anisotropic growth of surfaces and growth coupled with the solutions of PDEs

Exploring the Spatial&ndash;Temporal Variation in Cultivated Land Quality and Influential Factors in the Lower Reaches of the Yangtze River from 2017 to 2020

Cultivated land quality is directly related to national food security; hence, it is necessary to determine the spatial&ndash;temporal characteristics and factors that influence its variation. This study analyzed the soil properties and cultivated land quality in the Hang-Jia-Hu Plain, the most important grain production base in Zhejiang Province, located in the lower reaches of the Yangtze River, from 2017 to 2020. In addition, the factors that influenced cultivated land quality variation were explored. The results showed that soil pH and soil organic matter (SOM) significantly improved from 6.32 and 29.07 g/kg in 2017 to 6.38 and 31.54 g/kg in 2020, whereas the variations in available phosphorus (AP) and available potassium (AK) were not significant. More than 60% of the cultivated land still had the potential for soil nutrient status improvement. The cultivated land quality indicator (CLQI) calculated based on the national standard (GB/T 33469-2016) significantly increased from 0.90 in 2017 to 0.91 in 2020. According to the CLQI classification, approximately three quarters of the cultivated land was defined as high-yielding fields. Although the spatial pattern for CLQI was similar between 2017 and 2020, more than 75% of the cultivated land quality showed an increasing trend that was mainly located in the northeastern and central areas. The results of influential factor detection indicated that the improvement in SOM and available soil nutrients including AP and AK was the main reason for the CLQI increase, whereas the climate, topography, and socioeconomic factors had little influence on the change in CLQI. In addition, when influential factors interacted, a significant increase in the explanatory ability for CLQI was obtained, especially for the interaction of SOM and AP variation, which explained 41% of the CLQI variation. This study provides basic foundations and references for cultivated land quality monitoring and improvement in the lower reaches of the Yangtze River, China