Identification of serum metabolites associated with aristolochic acid nephropathy severity and insights into the underlying mechanism.

Aristolochic acid nephropathy (AAN) is a rapidly progressive kidney disease caused by medical or environmental exposure to aristolochic acids (AAs). This study aimed to identify serum metabolites associated with the severity of acute AAN and investigate the underlying mechanisms. Male C57BL/6 mice were treated with vehicle and 3 doses of aristolochic acid I (AAI) (1.25, 2.5, and 5 mg/kg/d) for 5 days by intraperitoneal injection. The results showed that AAI dose-dependently increased blood urea nitrogen (BUN) and serum creatinine (Scr) levels and renal pathological damage. Non-targeted metabolomics revealed that differences in serum metabolite profiles from controls increased with increasing AAI doses. Compared with the control group, 56 differentially expressed metabolites (DEMs) that could be affected by all 3 doses of AAI were obtained. We further identified 13 DEMs whose abundance significantly correlated with Scr and BUN levels and had good predictive values for diagnosing AAI exposure. Among the 13 DEMs, lipids and lipid-like molecules constituted the majority. Western blotting found that AAI suppressed renal fatty acid oxidation (FAO)-related enzymes expression. In conclusion, these findings provided evidence for developing biomarkers for monitoring AAs exposure and AAN diagnosis and indicated activation of FAO as a potential direction for the treatment of AAN.

Improving the identification of pollution source areas with catchment-resolution sensitivity analysis.

The significant impacts of total nitrogen (TN) and total phosphorus (TP) on riverine ecosystems underscores the critical need to identify the primary nutrient source areas in watersheds. This study aims to unravel the influences of terrain and land use types on mean monthly TN (TNM) and mean monthly TP (TPM) export across varying catchment resolutions in the Qiantang River Watershed of China. The findings of this study illuminated the critical role of topography in understanding nutrient dynamics, wielding a profound influence over water flow patterns and nutrient dispersion. Both land slope and Stream Power Index (SPI) displayed substantial negative correlations (r < -0.6) with TNM and TPM concentrations, whereas the Topographic Wetness Index (TWI) showed positive correlations with the nutrient indexes. In addition to terrain characteristics, impervious land surfaces had a positive correlation with nutrient concentrations, while grassland and forest areas exhibited negative correlations. Results further underscored the substantial influence of catchment resolution on correlations between watershed properties and riverine nutrient concentrations. It was imperative to choose an effective catchment resolution in watershed delineation - not too coarse, nor too fine - to accurately capture the topographic and land use impacts on nutrient dynamics. With the most appropriate catchment size (Catchment 700 km2), the critical pollution source areas for TN and TP pollution were identified, and thus could be used to guide future pollution reduction efforts. The study not only highlights the importance of identifying an appropriate catchment size for water pollution, but also emphasizes the necessity of effectively extracting critical pollution source areas to mitigate water nutrient pollution and increase the ecological integrity of the Qiantang River Watershed.

A novel constraint-based structure learning algorithm using marginal causal prior knowledge.

Causal discovery with prior knowledge is important for improving performance. We consider the incorporation of marginal causal relations, which correspond to the presence or absence of directed paths in a causal model. We propose the Marginal Prior Causal Knowledge PC (MPPC) algorithm to incorporate marginal causal relations into a constraint-based structure learning algorithm. We provide the theorems of conditional independence properties by combining observational data and marginal causal relations. We compare the MPPC algorithm with other structure learning methods in both simulation studies and real-world networks. The results indicate that, compare with other constraint-based structure learning methods, MPPC algorithm can incorporate marginal causal relations and is more effective and more efficient.

Nonbiodegradable microplastic types determine the diversity and structure of soil microbial communities: A meta-analysis.

As an emerging contaminant, microplastics (MPs) have received considerable attention for their potential threat to the soil environment. However, the response of soil bacterial and fungal communities to MPs exposure remains unclear. In this study, we conducted a global meta-analysis of 95 publications and 2317 observations to assess the effects of nonbiodegradable MP properties and exposure conditions on soil microbial biomass, alpha and beta diversity, and community structure. Our results indicate that MPs increased (p < 0.05) soil active microbial biomass by 42%, with the effect varying with MPs type, exposure concentration, exposure time and soil pH. MPs concentration was identified as the most important factor controlling the response of soil microbial biomass to MPs. MPs addition decreased (p < 0.05) the soil bacterial Shannon and Chao1 indices by 2% and 3%, respectively, but had limited effects (p > 0.05) on soil fungal Shannon and Chao1 indices. The type of MPs and exposure time determined the effects of MPs on bacterial Shannon and Chao1 indices, while the type of MPs and soil pH controlled the response ratios of fungal Shannon and Chao1 indices to MPs. Specifically, soil organic carbon (SOC) was the major factor regulating the response ratio of bacterial alpha diversity index to MPs. The presence of MPs did not affect soil bacterial community structure and beta diversity. Our results highlight that MPs reduced bacterial diversity and richness but increased the soil active microbial biomass, suggesting that MPs could disrupt biogeochemical cycles by promoting the growth of specific microorganisms.

Mineralization and microbial utilization of poly(lactic acid) microplastic in soil.

The current carbon dioxide (CO2) evolution-based standard method for determining biodegradable microplastics (MPs) degradation neglects its priming effect on soil organic matter decomposition, which misestimates their biodegradability. Here, a 13C natural abundance method was used to estimate the mineralization of poly(lactic acid) (PLA) MP in various agricultural soils, and to trace its utilization in different microbial groups. In alkaline soils, the PLA-derived CO2 emissions increased with increasing soil carbon/nitrogen (C/N) ratios, and the mineralization of PLA MP concentrations ranged from 3-33 %, whereas the CO2 evolution method probably over- or under-estimated the mineralization of PLA in alkaline soils with different soil C/N ratios. Low PLA mineralization (1-5 %) were found in the acidic soil, and the standard method largely overestimated the mineralization of PLA MP by 1.3- to 3.3-fold. Moreover, the hydrolysate of PLA MP was preferentially assimilated by Gram-negative bacteria, but Gram-positive bacterial decomposition mainly contributed to the release of PLA-derived CO2 at low MP concentrations (≤ 1 %). Overall, the 13C natural abundance method appears to be suitable for tracking the mineralization and microbial utilization of biodegradable PLA in soils, and the PLA-derived C is mainly assimilated and decomposed by bacterial groups.

Stable, Scalable, and Free-Standing Perovskite Quantum Dots Composite Reinforced by Cellulose Fibers.

Perovskite quantum dots (PQDs) have attracted emerging attention as fluorescent and light-absorbing materials for next-generation optoelectronics due to their outstanding properties and cost-efficiency. However, PQD thin film suffers significant instability due to structure and material failures, which hinders their application in flexible and reliable PQD-based advanced wearable devices. Herein, we use commercial cellulose fiber-based filter paper as a substrate to synthesize PQDs in situ and fabricate PQD-paper free-standing flexible composite film. The abundant hydroxy capping ligands of cellulose fibers and the unique dense network structure of the filter paper can facilitate confined crystallization, forming strong interactions between the PQDs and substrate, the unpackaged PQD composite film showed extraordinary stability (>30 days) in the air with high humidity (90%). Meanwhile, the strong interaction between PQDs and paper enables an ultrasimple drop-cast synthesis process with excellent process tolerance, making it customizable and easy to scale up (10 cm in diameter). Due to the uniformly dispersed PQDs on cellulose fibers of the substrate, the composite demonstrates impressive photo-responsive properties. Photodetector (PD) arrays were designed on free-standing PQD paper and flexible graphitic electrodes, and circuits were fabricated by drawing. The PD arrays can work as optical and electrical dual-mode image sensors with incredible bending robustness, enduring up to 100,000 cycles at 180°.

Spatial distribution of PAHs and microbial communities in intertidal sediments of the Pearl River Estuary, South China.

The exploration of sediment pollution caused by PAHs and its impact on microbial communities can provide valuable insights for the remediation of sediments. The spatial distribution of PAHs and their impact on the microbial community within the Pearl River Estuary were investigated in this study. The findings revealed that the total concentration ranges of 16 PAHs were between 24.26 and 3075.93 ng/g, with naphthalene, fluorene, and phenanthrene potentially exerting adverse biological effects. More PAHs were found to accumulate in subsurface sediments, and their average accumulation rates gradually decreased as the number of rings in PAHs increased, ranging from 180 % for 2-ring to 36 % for 6-ring. The phyla Proteobacteria, Bacteroidetes, Actinobacteria, and Chloroflexi were found to dominate both surface and subsurface sediments The correlation between microbial genera and PAHs contents was weak in sediments with low levels of PAHs contamination, while a more significant positive relationship was observed in sediments with high levels of PAHs contamination. The physicochemical properties of sediments, such as pH, soil structure and Cu significantly influence bacterial community composition in highly contaminated sediments. Additionally, the network analysis revealed that certain bacterial genera, including Novosphingobium, Robiginitalea and Synechococcus_CC9902, played a pivotal role in the degradation of PAHs. These findings are significant in comprehending the correlation between bacterial communities and environmental factors in intertidal ecosystems, and establish a scientific foundation for bioremediation of intertidal zones.

Comprehensive evaluation of genes related to basement membrane in hepatocellular carcinoma.

In all mammals, the basement membrane serves as a pivotal extracellular matrix. Hepatocellular carcinoma (HCC) is a challenge among numerous cancer types shaped by basement membrane-related genes (BMGs). Our research established an innovative prognostic model that is highly accurate in its prediction of HCC prognoses and immunotherapy efficacy to summarize the crucial role of BMGs in HCC. We obtained HCC transcriptome analysis data and corresponding clinical data from The Cancer Genome Atlas (TCGA). To augment our dataset, we incorporated 222 differentially expressed BMGs identified from relevant literature. A weighted gene coexpression network analysis (WGCNA) of 10158 genes demonstrated four modules that were connected to HCC. Additionally, 66 genes that are found at the intersection of BMGs and HCC-related genes were designated as hub HCC-related BMGs. MMP1, ITGA2, P3H1, and CTSA comprise the novel model that was engineered using univariate and multivariate Cox regression analysis. Furthermore, the International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) datasets encouraged the BMs model's validity. The overall survival (OS) of individuals with HCC may be precisely predicted in the TCGA and ICGC databases utilizing the BMs model. A nomogram based on the model was created in the TCGA database at similar time, and displayed a favorable discriminating ability for HCC. Particularly, when compared to the patients at an elevated risk, the patients with a low-risk profile presented different tumor microenvironment (TME) and hallmark pathways. Moreover, we discovered that a lower risk score of HCC patients would display a greater response to immunotherapy. Finally, quantitative real-time PCR (qRT-PCR) experiments were used to verify the expression patterns of BMs model. In summary, BMs model demonstrated efficacy in prognosticating the survival probability of HCC patients and their immunotherapeutic responsiveness.

Spatio-temporal evolution mechanism and dynamic simulation of nitrogen and phosphorus pollution of the Yangtze River economic Belt in China.

Excess nitrogen and phosphorus inputs are the main causes of aquatic environmental deterioration. Accurately quantifying and dynamically assessing the regional nitrogen and phosphorus pollution emission (NPPE) loads and influencing factors is crucial for local authorities to implement and formulate refined pollution reduction management strategies. In this study, we constructed a methodological framework for evaluating the spatio-temporal evolution mechanism and dynamic simulation of NPPE. We investigated the spatio-temporal evolution mechanism and influencing factors of NPPE in the Yangtze River Economic Belt (YREB) of China through the pollution load accounting model, spatial correlation analysis model, geographical detector model, back propagation neural network model, and trend analysis model. The results show that the NPPE inputs in the YREB exhibit a general trend of first rising and then falling, with uneven development among various cities in each province. Nonpoint sources are the largest source of land-based NPPE. Overall, positive spatial clustering of NPPE is observed in the cities of the YREB, and there is a certain enhancement in clustering. The GDP of the primary industry and cultivated area are important human activity factors affecting the spatial distribution of NPPE, with economic factors exerting the greatest influence on the NPPE. In the future, the change in NPPE in the YREB at the provincial level is slight, while the nitrogen pollution emissions at the municipal level will develop towards a polarization trend. Most cities in the middle and lower reaches of the YREB in 2035 will exhibit medium to high emissions. This study provides a scientific basis for the control of regional NPPE, and it is necessary to strengthen cooperation and coordination among cities in the future, jointly improve the nitrogen and phosphorus pollution tracing and control management system, and achieve regional sustainable development.

Numerical investigation on the impact of different coronary aneurysms morphologies on thrombus formation and hemodynamics: a comparative study.

Coronary artery aneurysms (CAAs) are morphologically classified as saccular and fusiform. There is still a great deal of clinical controversy as to which types of CAA are more likely to cause thrombosis. Therefore, the main objective of this study was to evaluate the trend of thrombus growth in CAAs with different morphologies and to assess the risk of possible long-term complications based on hemodynamic parameters. Utilizing computed tomography angiography (CTA) data from eight healthy coronary arteries, two distinct morphologies of coronary artery aneurysms (CAAs) were reconstructed. Distribution of four wall shear stress (WSS)-based indicators and three helicity indicators was analyzed in this study. Meanwhile, a thrombus growth model was introduced to analyze the thrombus formation in CAAs with different morphologies. The research results showed the distribution of most WSS indicators between saccular and fusiform CAAs was not statistically significant. However, due to the presence of a more pronounced helical flow pattern, irregular helical flow structure and longer time of flow stagnation in saccular CAAs during the cardiac cycle, the mean and maximum relative residence time (RRT) were significantly higher in saccular CAAs than in fusiform CAAs (P < 0.05). This may increase the risk of saccular coronary arteries leading to aneurysmal dilatation or even rupture. Although the two CAAs had similar rates of thrombosis, fusiform CAAs may more early cause obstruction of the main coronary flow channel where the aneurysm is located due to thrombosis growth. Thus, the risk of thrombosis in fusiform coronary aneurysms may warrant greater clinical concern.

Delineation of renal protein profiles in aristolochic acid I-induced nephrotoxicity in mice by label-free quantitative proteomics.

Introduction: Aristolochic acid nephropathy (AAN) is a kidney injury syndrome caused by aristolochic acids exposure. Our study used label-free quantitative proteomics to delineate renal protein profiles and identify key proteins after exposure to different doses of aristolochic acid I (AAI). Methods: Male C57BL/6 mice received AAI (1.25 mg/kg/d, 2.5 mg/kg/d, or 5 mg/kg/d) or vehicle for 5 days. Results and discussion: The results showed that AAI induced dose-dependent nephrotoxicity. Differences in renal protein profiles between the control and AAI groups increased with AAI dose. Comparing the control with the low-, medium-, and high-dose AAI groups, we found 58, 210, and 271 differentially expressed proteins, respectively. Furthermore, protein-protein interaction network analysis identified acyl-CoA synthetase medium-chain family member 3 (Acsm3), cytochrome P450 family 2 subfamily E member 1 (Cyp2e1), microsomal glutathione S-transferase 1 (Mgst1), and fetuin B (Fetub) as the key proteins. Proteomics revealed that AAI decreased Acsm3 and Cyp2e1 while increasing Mgst1 and Fetub expression in mice kidneys, which was further confirmed by Western blotting. Collectively, in AAI-induced nephrotoxicity, renal protein profiles were dysregulated and exacerbated with increasing AAI dose. Acsm3, Cyp2e1, Mgst1, and Fetub may be the potential therapeutic targets for AAN.

Deep Learning Radiomics Model of Contrast-Enhanced CT for Differentiating the Primary Source of Liver Metastases.

RATIONALE AND OBJECTIVES: To develop and validate a deep learning radiomics (DLR) model based on contrast-enhanced computed tomography (CT) to identify the primary source of liver metastases. MATERIALS AND METHODS: In total, 657 liver metastatic lesions, including breast cancer (BC), lung cancer (LC), colorectal cancer (CRC), gastric cancer (GC), and pancreatic cancer (PC), from 428 patients were collected at three clinical centers from January 2018 to October 2023 series. The lesions were randomly assigned to the training and validation sets in a 7:3 ratio. An additional 112 lesions from 61 patients at another clinical center served as an external test set. A DLR model based on contrast-enhanced CT of the liver was developed to distinguish the five pathological types of liver metastases. Stepwise classification was performed to improve the classification efficiency of the model. Lesions were first classified as digestive tract cancer (DTC) and non-digestive tract cancer (non-DTC). DTCs were divided into CRC, GC, and PC and non-DTCs were divided into LC and BC. To verify the feasibility of the DLR model, we trained classical machine learning (ML) models as comparison models. Model performance was evaluated using accuracy (ACC) and area under the receiver operating characteristic curve (AUC). RESULTS: The classification model constructed by the DLR algorithm showed excellent performance in the classification task compared to ML models. Among the five categories task, highest ACC and average AUC were achieved at 0.563 and 0.796 in the validation set, respectively. In the DTC and non-DTC and the LC and BC classification tasks, AUC was achieved at 0.907 and 0.809 and ACC was achieved at 0.843 and 0.772, respectively. In the CRC, GC, and PC classification task, ACC and average AUC were the highest, at 0.714 and 0.811, respectively. CONCLUSION: The DLR model is an effective method for identifying the primary source of liver metastases.

Reniformin A suppresses non-small cell lung cancer progression by inducing TLR4/NLRP3/caspase-1/GSDMD-dependent pyroptosis.

Pyroptosis is an inflammatory form of programmed cell death that plays an important role in regulating tumor progression. Reniformin A (RA) is a natural compound isolated from the medicinal herb Isodon excisoides that has been applied as folk medicine in the treatment of esophageal cancer. However, whether RA has an individual function in cancer and the molecular mechanisms remain unclear. Here, we show that in non-small-cell lung cancer (NSCLC), RA inhibits tumor growth by functioning as a pyroptosis inducer to promote TLR4/NLRP3/caspase-1/GSDMD axis. Specially, RA treatment increased Toll-like receptor 4 (TLR4) protein expression level by enhancing the TLR4 stability. Based on the molecular docking, we identified that RA directly bound to TLR4 to activate the NLRP3 inflammasome and promote pyroptosis in A549 cells. Moreover, TLR4 is essential for RA-induced pyroptosis, and loss of TLR4 abolished RA-induced pyroptosis and further reduced the inhibitory effect of RA on NSCLC. In vivo experiments confirmed that RA inhibited the growth of lung tumors in mice by affecting pyroptosis in a dose-dependent manner. Furthermore, TLR4 knockdown abolished RA-induced pyroptosis and inhibited the effect of RA chemotherapy in vivo. In conclusion, we propose that RA has a significant anticancer effect in NSCLC by inducing TLR4/NLRP3/caspase-1/GSDMD-mediated pyroptosis, which may provide a potential strategy for the treatment of NSCLC.

A global meta-analysis on the drivers of salt marsh planting success and implications for ecosystem services.

Planting has been widely adopted to battle the loss of salt marshes and to establish living shorelines. However, the drivers of success in salt marsh planting and their ecological effects are poorly understood at the global scale. Here, we assemble a global database, encompassing 22,074 observations reported in 210 studies, to examine the drivers and impacts of salt marsh planting. We show that, on average, 53% of plantings survived globally, and plant survival and growth can be enhanced by careful design of sites, species selection, and novel planted technologies. Planting enhances shoreline protection, primary productivity, soil carbon storage, biodiversity conservation and fishery production (effect sizes = 0.61, 1.55, 0.21, 0.10 and 1.01, respectively), compared with degraded wetlands. However, the ecosystem services of planted marshes, except for shoreline protection, have not yet fully recovered compared with natural wetlands (effect size = -0.25, 95% CI -0.29, -0.22). Fortunately, the levels of most ecological functions related to climate change mitigation and biodiversity increase with plantation age when compared with natural wetlands, and achieve equivalence to natural wetlands after 5-25 years. Overall, our results suggest that salt marsh planting could be used as a strategy to enhance shoreline protection, biodiversity conservation and carbon sequestration.

Engineering sulfur defective Bi2S3@C with remarkably enhanced electrochemical kinetics of lithium-ion batteries.

Introducing the appropriate vacancies to augment the active sites and improve the electrochemical kinetics while maintaining high cyclability is a major challenge for its widespread application in electrochemical energy storage. Here, core-shell structured Bi2S3@C with sulfur vacancies was prepared by hydrothermal method and one-step carbonization/sulfuration process, which significantly improves the intrinsic electrical conductivity and ion transport efficiency of Bi2S3. Additionally, the uniform protective carbon layer around surface of composite maintains structural stability and effectively alleviates volume expansion during alloying/dealloying. As a result, the BSC-500 anode exhibits a brilliant reversible capacity of 636 mAh/g at 0.2 A/g and a long-term stable capacity of 524 mAh/g for 500 cycles at a high current density of 3 A/g in lithium-ion batteries. In addition, the assembled Bi2S3@C//LiCoO2 full cell delivered a capacity of 184 mAh/g at 1 A/g and excellent cyclability (125 mAh/g after 1000 cycles). The proposed strategy of combining sulfur vacancies with a core-shell structure to improve the electrochemical kinetics of Bi2S3 in lithium-ion batteries off the prospect for practical applications of transition metal sulfide anodes.

Identifying Pathological Subtypes of Brain Metastasis from Lung Cancer Using MRI-Based Deep Learning Approach: A Multicenter Study.

The aim of this study was to investigate the feasibility of deep learning (DL) based on multiparametric MRI to differentiate the pathological subtypes of brain metastasis (BM) in lung cancer patients. This retrospective analysis collected 246 patients (456 BMs) from five medical centers from July 2016 to June 2022. The BMs were from small-cell lung cancer (SCLC, n = 230) and non-small-cell lung cancer (NSCLC, n = 226; 119 adenocarcinoma and 107 squamous cell carcinoma). Patients from four medical centers were assigned to training set and internal validation set with a ratio of 4:1, and we selected another medical center as an external test set. An attention-guided residual fusion network (ARFN) model for T1WI, T2WI, T2-FLAIR, DWI, and contrast-enhanced T1WI based on the ResNet-18 basic network was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. Compared with models based on five single-sequence and other combinations, a multiparametric MRI model based on five sequences had higher specificity in distinguishing BMs from different types of lung cancer. In the internal validation and external test sets, AUCs of the model for the classification of SCLC and NSCLC brain metastasis were 0.796 and 0.751, respectively; in terms of differentiating adenocarcinoma from squamous cell carcinoma BMs, the AUC values of the prediction models combining the five sequences were 0.771 and 0.738, respectively. DL together with multiparametric MRI has discriminatory feasibility in identifying pathology type of BM from lung cancer.

New insights into the concentration-dependent regulation of membrane biofouling formation via continuous nanoplastics stimulation.

The release of nanoplastics (NPs) into the environment is growing due to the extensive use of plastic products. Numerous studies have confirmed the negative effects of NPs on microorganisms, which poses uncertainties concerning their impact on nanofiltration (NF) membrane biofouling. This study investigated the initial cell adhesion process, NF membrane biofouling kinetic processes and bacterial responses of Pseudomonas aeruginosa (P. aeruginosa) exposed to varied NPs concentrations (0-50 mg·L-1). Transcriptome analysis demonstrated that low concentration of NPs (0.1 mg·L-1) promoted bacterial quorum sensing, energy metabolism, exopolysaccharide biosynthesis and bacterial secretion systems. Correspondingly, the polysaccharide content increased remarkably to 2.77 times the unexposed control, which served as a protective barrier for bacteria to avoid the impact of NPs-induced stress. Suppressed homologous recombination, microbial metabolic potentials and flagellar assembly were detected in bacteria exposed to a high concentration (50 mg·L-1) of NPs, mainly due to the triggered reactive oxygen species (ROS) generation, genomic DNA damage, and decreased energy production. Overall, enhanced formation of the extracellular polymeric substances (EPS) and aggravated membrane flux decline were observed when NPs interacted with the membrane surface by cell secretions (low NPs levels) or cell lysis (high NPs levels). These findings shed light on understanding the microbial metabolism mechanism and membrane biofouling propensity with NPs stress at both the molecular and gene levels.

Molecular basis and current insights of atypical Rho small GTPase in cancer.

Atypical Rho GTPases are a subtype of the Rho GTPase family that are involved in diverse cellular processes. The typical Rho GTPases, led by RhoA, Rac1 and Cdc42, have been well studied, while relative studies on atypical Rho GTPases are relatively still limited and have great exploration potential. With the increase in studies, current evidence suggests that atypical Rho GTPases regulate multiple biological processes and play important roles in the occurrence and development of human cancers. Therefore, this review mainly discusses the molecular basis of atypical Rho GTPases and their roles in cancer. We summarize the sequence characteristics, subcellular localization and biological functions of each atypical Rho GTPase. Moreover, we review the recent advances and potential mechanisms of atypical Rho GTPases in the development of multiple cancers. A comprehensive understanding and extensive exploration of the biological functions of atypical Rho GTPases and their molecular mechanisms in tumors will provide important insights into the pathophysiology of tumors and the development of cancer therapeutic strategies.

TRIM28 promotes porcine epidemic diarrhea virus replication by mitophagy-mediated inhibition of the JAK-STAT1 pathway.

Porcine epidemic diarrhea virus (PEDV) infection causes immunosuppression and clinical symptoms such as vomiting, watery diarrhea, dehydration, and even death in piglets. TRIM28, an E3 ubiquitin ligase, is involved in the regulation of autophagy. However, the role of TRIM28 in PEDV infection is unknown. This study aimed to determine whether TRIM28 acts as a host factor for PEDV immune escape. We found that depletion of TRIM28 inhibited PEDV replication, whereas overexpression of TRIM28 promoted the viral replication in host cells. Furthermore, knockdown of TRIM28 reversed PEDV-induced downregulation of the JAK/STAT1 pathway. Treatment with the mitophagic activator carbonyl cyanide 3-chlorophenylhydrazone (CCCP) attenuated the activating effect of TRIM28 depletion on the expression of the STAT1 pathway-related proteins. Treatment with CCCP also reduced the nuclear translocation of pSTAT1. Moreover, TRIM28, via its RING domain, interacted with PEDV N. Overexpression of TRIM28 induced mitophagy, which could be enhanced by co-expression with PEDV N. The results indicate that PEDV infection upregulates the expression of TRIM28, which induces mitophagy, leading to inhibition of the JAK-STAT1 pathway. This research unveils a new mechanism by which PEDV can hijack host cellular TRIM28 to promote its own replication.

Spleen tyrosine kinase facilitates the progression of papillary thyroid cancer regulated by the hsa_circ_0006417/miR-377-3p axis.

Papillary thyroid cancer (PTC) is a prevalent malignancy worldwide. Spleen tyrosine kinase (SYK) is a crucial enzyme that participates in various biological processes, including cancer progression. This study aims to uncover the biological function of SYK in PTC. SYK expression patterns in PTC were evaluated using quantitative real time polymerase chain reaction (qRT-PCR), immunohistochemistry (IHC), and western blot. Cell function assays were performed to assess the effects of SYK on PTC. Bioinformatics analysis was conducted to identify intriguing microRNA (miRNA) and circular RNA (circRNA). Dual-Luciferase Reporter or RNA immunoprecipitation assays were used to investigate the correlation among SYK, miR-377-3p, and hsa_circ_0006417. SYK was upregulated in PTC. Overexpression of SYK exhibited a positive correlation with tumor size, lymph node metastasis, and unfavorable disease-free survival. Functional assays revealed that SYK exerted tumorigenic effect on PTC cells through mTOR/4E-BP1 pathway. Mechanistically, hsa_circ_0006417 and miR-377-3p regulated SYK expression, offering modulating its tumor-promoting effects. Collectively, SYK acts as an oncogene in PTC through mTOR/4E-BP1 pathway, which is regulated by the hsa_circ_0006417/miR-377-3p axis, thereby providing a potential alternative for PTC treatment.