Effects of rice-prawn (Macrobrachium nipponense) co-culture on the microbial community of soil.

In the Lixiahe region of China, co-culture has been rapidly promoted in flooded paddy fields owing to its ecological and economic benefits. Rice-prawn co-culture can reduce the damage of crab and shrimp to rice growth and paddy field and substantially change the soil microbial community and soil fertility. In this study, we compared changes in the soil microbial community and soil fertility in waterlogged paddies under conventional rice culture (CR), rice-prawn (Macrobrachium nipponense) co-culture (RP), and pond culture (PC). The microbial abundance in RP was significantly higher than that in CR. RP soil microbial diversity was significantly higher than PC soil microbial diversity. The dominant bacteria in RP soil were Proteobacteria, Chloroflexi, and Bacteroidetes. Compared with those in CR, total organic matter (TOM) and total nitrogen in RP were relatively stable, available potassium and available phosphorus (AP) decreased, and other indicators increased significantly. Soil fertility significantly benefited from co-culture, with total organic carbon (TOC) increasing. Interactive relationship analysis showed that TOM, TOC, AP, and NH4+-N were the main factors affecting the microbial community. Co-occurrence network analyses showed that network modularity increased with co-culture, indicating that a unique soil microbial community formed under co-culture, improving the adaptability and tolerance to co-culture. Thus, RP is a suitable culture method for this commercially important species. The results of this study can inform the practical operation of fertilizer use and sustainable development of rice-prawn aquaculture systems. KEY POINTS: • Microbial abundance and diversity increased under rice-prawn co-culture. • Co-culture significantly improved soil fertility, with an increase in TOC. • Rice-prawn co-culture is an ecologically suitable culture method for prawns.

admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties.

SUMMARY: admetSAR was developed as a comprehensive source and free tool for the prediction of chemical ADMET properties. Since its first release in 2012 containing 27 predictive models, admetSAR has been widely used in chemical and pharmaceutical fields. This update, admetSAR 2.0, focuses on extension and optimization of existing models with significant quantity and quality improvement on training data. Now 47 models are available for either drug discovery or environmental risk assessment. In addition, we added a new module named ADMETopt for lead optimization based on predicted ADMET properties. AVAILABILITY AND IMPLEMENTATION: Free available on the web at http://lmmd.ecust.edu.cn/admetsar2/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Oral mosapride can provide additional anti-emetic efficacy following total joint arthroplasty under general anesthesia: a randomized, double-blinded clinical trial.

BACKGROUND: We sought to determine (1) whether the addition of prophylactic oral mosapride to a protocol including dexamethasone and ondansetron further reduces postoperative nausea and vomiting (PONV) compared with ondansetron alone or the combination of both; (2) whether preemptive application of oral mosapride provides additional clinical benefits for bowel function and appetite, thus improving functional recovery. METHODS: We randomized 240 patients undergoing total hip and knee arthroplasty to receive placebo (Control, n = 80), dexamethasone (10 mg) before anesthesia induction (Dexa, n = 82), or dexamethasone (10 mg) before anesthesia induction as well as oral mosapride (5 mg) before and after surgery (Mosa+Dexa, n = 78). Patients were assessed at 0-6, 6-12, 12-24, and 24-48 h postoperatively. Primary outcomes were incidence and severity of PONV as well as complete response. Secondary outcomes were appetite, time until first defecation and ambulation, patient satisfaction score, and length of hospital stay. RESULTS: Mosa+Dexa patients showed significantly lower incidence of nausea at 6-12 h (3.8%) and over the entire evaluation period (6.4%), as well as a higher rate of complete response (89.7%) than other patients. Mosa+Dexa patients required less time to achieve first defecation and ambulation, they were hospitalized for shorter time, and they were more satisfied with clinical care. CONCLUSION: Addition of oral mosapride further reduced incidence of PONV, especially postoperative nausea, during 6-12 h postoperatively. Moreover, preemptive application of oral mosapride can further improve appetite, bowel function, ambulation and length of hospital stay. TRIAL REGISTRATION: The study protocol was registered at the Chinese Clinical Trial Registry ( ChiCTR1800015896 ), prospectively registered on 27/04/2018.

LINC00969 promotes the degeneration of intervertebral disk by sponging miR-335-3p and regulating NLRP3 inflammasome activation.

Long noncoding RNAs (LncRNAs) may serve as miRNA sponges to regulate the expressions of miRNA target genes. LncRNA LINC00969 has been indicated to be upregulated in intervertebral disk degeneration. However, the regulatory mechanism of LINC00969 in intervertebral disk degeneration progression remains unclear. Differently expressed LINC00969, miR-335-3p, and thioredoxin-interacting protein (TXNIP) were determined in nucleus pulposus (NP) tissues and cells isolated from patients with intervertebral disk degeneration. The interaction between LINC00969, miR-335-3p, and TXNIP was also assessed. In this study, we demonstrated that LINC00969 was highly expressed, whereas miR-335-3p was aberrantly downregulated in NP tissues and cells of intervertebral disk degeneration patients. In addition, our results suggested that LINC00969 enhanced NP cell apoptosis. More importantly, LINC00969 was identified to function as a competitive endogenous RNA (ceRNA) for miR-335-3p to positively regulate TXNIP expression in vitro. Our study provided evidence for the cross-talk between LINC00969, miR-335-3p, and TXNIP, shedding light on the therapy for intervertebral disk degeneration. © 2018 IUBMB Life, 71(5):611-618, 2019.

In Silico Prediction of Endocrine Disrupting Chemicals Using Single-Label and Multilabel Models.

Endocrine disruption (ED) has become a serious public health issue and also poses a significant threat to the ecosystem. Due to complex mechanisms of ED, traditional in silico models focusing on only one mechanism are insufficient for detection of endocrine disrupting chemicals (EDCs), let alone offering an overview of possible action mechanisms for a known EDC. To remove these limitations, in this study both single-label and multilabel models were constructed across six ED targets, namely, AR (androgen receptor), ER (estrogen receptor alpha), TR (thyroid receptor), GR (glucocorticoid receptor), PPARg (peroxisome proliferator-activated receptor gamma), and aromatase. Two machine learning methods were used to build the single-label models, with multiple random under-sampling combining voting classification to overcome the challenge of data imbalance. Four methods were explored to construct the multilabel models that can predict the interaction of one EDC against multiple targets simultaneously. The single-label models of all the six targets have achieved reasonable performance with balanced accuracy (BA) values from 0.742 to 0.816. Each top single-label model was then joined to predict the multilabel test set with BA values from 0.586 to 0.711. The multilabel models could offer a significant boost over the single-label baselines with BA values for the multilabel test set from 0.659 to 0.832. Therefore, we concluded that single-label models could be employed for identification of potential EDCs, while multilabel ones are preferable for prediction of possible mechanisms of known EDCs.

Computational Prediction of Site of Metabolism for UGT-Catalyzed Reactions.

The investigation of metabolically liable sites of xenobiotics mediated by UDP-glucuronosyltransferases (UGTs) is important for lead optimization in early drug discovery. However, it is time-consuming and costly to identify potentially susceptible sites experimentally. Hence, in silico approaches have been developed to predict the site of metabolism (SOM) of UGT-catalyzed substrates. In the present work, four major types of reactions catalyzed by UGTs were collected from the Handbook of Metabolic Pathways of Xenobiotics along with their corresponding glucuronidation products. These observed and nonobserved SOMs were identified and encoded by atom environment fingerprints. Four resampling methods were performed to treat data imbalance, while four feature selection methods were utilized to choose appropriate features. Three tree-form machine learning algorithms were conducted to build discriminating models, and optimal models were then obtained for the different types of reaction. The results indicated that all of the chosen best models showed area under the curve values ranging from 0.713 to 0.869 for two independent external validation sets. Our study could supply valuable information for optimization of pharmacokinetic profiles and contribute to metabolism prediction.

Multiclassification Prediction of Enzymatic Reactions for Oxidoreductases and Hydrolases Using Reaction Fingerprints and Machine Learning Methods.

Drug metabolism is a complex procedure in the human body, including a series of enzymatically catalyzed reactions. However, it is costly and time consuming to investigate drug metabolism experimentally; computational methods are hence developed to predict drug metabolism and have shown great advantages. As the first step, classification of metabolic reactions and enzymes is highly desirable for drug metabolism prediction. In this study, we developed multiclassification models for prediction of reaction types catalyzed by oxidoreductases and hydrolases, in which three reaction fingerprints were used to describe the reactions and seven machine learnings algorithms were employed for model building. Data retrieved from KEGG containing 1055 hydrolysis and 2510 redox reactions were used to build the models, respectively. The external validation data consisted of 213 hydrolysis and 512 redox reactions extracted from the Rhea database. The best models were built by neural network or logistic regression with a 2048-bit transformation reaction fingerprint. The predictive accuracies of the main class, subclass, and superclass classification models on external validation sets were all above 90%. This study will be very helpful for enzymatic reaction annotation and further study on metabolism prediction.

In Silico Prediction of Chemicals Binding to Aromatase with Machine Learning Methods.

Environmental chemicals may affect endocrine systems through multiple mechanisms, one of which is via effects on aromatase (also known as CYP19A1), an enzyme critical for maintaining the normal balance of estrogens and androgens in the body. Therefore, rapid and efficient identification of aromatase-related endocrine disrupting chemicals (EDCs) is important for toxicology and environment risk assessment. In this study, on the basis of the Tox21 10K compound library, in silico classification models for predicting aromatase binders/nonbinders were constructed by machine learning methods. To improve the prediction ability of the models, a combined classifier (CC) strategy that combines different independent machine learning methods was adopted. Performances of the models were measured by test and external validation sets containing 1336 and 216 chemicals, respectively. The best model was obtained with the MACCS (Molecular Access System) fingerprint and CC method, which exhibited an accuracy of 0.84 for the test set and 0.91 for the external validation set. Additionally, several representative substructures for characterizing aromatase binders, such as ketone, lactone, and nitrogen-containing derivatives, were identified using information gain and substructure frequency analysis. Our study provided a systematic assessment of chemicals binding to aromatase. The built models can be helpful to rapidly identify potential EDCs targeting aromatase.

Computational Investigation of Ligand Binding to the Peripheral Site in CYP3A4: Conformational Dynamics and Inhibitor Discovery.

Human cytochrome P450 3A4 (CYP3A4) is a major drug-metabolizing enzyme responsible for the metabolism of ∼50% of clinically used drugs and is often involved in drug-drug interactions. It exhibits atypical binding and kinetic behavior toward many ligands. Binding of ligands to CYP3A4 is a complex process. Recent studies from both crystallography and biochemistry suggested the existence of a peripheral ligand-binding site at the enzyme surface. However, the stability of the ligand bound at this peripheral site and the possibility of discovering new CYP3A4 ligands based on this site remain unclear. In this study, we employed a combination of molecular docking, multiparalleled molecular dynamics (MD) simulations, virtual screening, and experimental bioassay to investigate these issues. Our results revealed that the binding mode of progesterone (PGS), a substrate of CYP3A4, in the crystal structure was not stable and underwent a significant conformational change. Through Glide docking and MD refinement, it was found that PGS was able to stably bind at the peripheral site via contacts with Phe215, Phe219, Phe220, and Asp214. On the basis of the refined peripheral site, virtual screening was then performed against the Enamine database. A total of three compounds were finally found to have inhibitory activity against CYP3A4 in both human liver microsome and recombinant human CYP3A4 enzyme assays, one of which showed potent inhibitory activity with IC50 lower than 1 µM and two of which exhibited moderate inhibitory activity with IC50 values lower than 10 µM. The findings not only presented the dynamic behavior of PGS at the peripheral site but also demonstrated the first indication of discovering CYP3A4 inhibitors based on the peripheral site.

Insights into the molecular mechanisms of Polygonum multiflorum Thunb-induced liver injury: a computational systems toxicology approach.

An increasing number of cases of herb-induced liver injury (HILI) have been reported, presenting new clinical challenges. In this study, taking Polygonum multiflorum Thunb (PmT) as an example, we proposed a computational systems toxicology approach to explore the molecular mechanisms of HILI. First, the chemical components of PmT were extracted from 3 main TCM databases as well as the literature related to natural products. Then, the known targets were collected through data integration, and the potential compound-target interactions (CTIs) were predicted using our substructure-drug-target network-based inference (SDTNBI) method. After screening for hepatotoxicity-related genes by assessing the symptoms of HILI, a compound-target interaction network was constructed. A scoring function, namely, Ascore, was developed to estimate the toxicity of chemicals in the liver. We conducted network analysis to determine the possible mechanisms of the biphasic effects using the analysis tools, including BiNGO, pathway enrichment, organ distribution analysis and predictions of interactions with CYP450 enzymes. Among the chemical components of PmT, 54 components with good intestinal absorption were used for analysis, and 2939 CTIs were obtained. After analyzing the mRNA expression data in the BioGPS database, 1599 CTIs and 125 targets related to liver diseases were identified. In the top 15 compounds, seven with Ascore values >3000 (emodin, quercetin, apigenin, resveratrol, gallic acid, kaempferol and luteolin) were obviously associated with hepatotoxicity. The results from the pathway enrichment analysis suggest that multiple interactions between apoptosis and metabolism may underlie PmT-induced liver injury. Many of the pathways have been verified in specific compounds, such as glutathione metabolism, cytochrome P450 metabolism, and the p53 pathway, among others. Hepatitis symptoms, the perturbation of nine bile acids and yellow or tawny urine also had corresponding pathways, justifying our method. In conclusion, this computational systems toxicology method reveals possible toxic components and could be very helpful for understanding the mechanisms of HILI. In this way, the method might also facilitate the identification of novel hepatotoxic herbs.

[Circumcision with no-flip Shang Ring technique for adult males: analysis of 168 cases].

OBJECTIVE: To observe the clinical effects of the no-flip procedure with the Chinese Shang Ring when circumcising adult males with redundant prepuce or phimosis, and to discuss its advantages and disadvantages. METHODS: Using the no-flip Shang Ring technique, we performed circumcision for 167 adult males aged 18 -72 (mean 27.8) years with redundant prepuce or phimosis, and analyzed the clinical data, including the operation time, postoperative complications, ring-removal time, and postoperative appearance of the penis. RESULTS: Complete follow-up data of 94 cases (56.29%) were obtained. The mean operation time was (5.03 +/- 0.71) minutes and the average ring-removal time was (18.83 +/- 6.70) days. The primary postoperative complications were edema (35 cases [37.23%] at 2 weeks and 9 cases [9.57%] at 4 weeks), including 2 severe cases (2.13%), and infection (3 cases [3.19%]). The pain scores were 2.01 +/- 2.46 during the procedure and 4.52 +/- 2.53 at 24 hours postoperatively. Slipping of the outer ring occurred in 1 case, and delayed removal of the ring in 30 cases (31.91%). CONCLUSION: Adult male circumcision with the no-flip Shang Ring technique is recommended for its short operation time, simple procedure, fewer postoperative complications, less pain, and better incision appearance.

Influence of Interface Modification on the Moisture Absorption and Thermal Resistance of Ramie Fiber/Degradable Epoxy Composites.

Natural fiber/degradable epoxy composites have received much attention for their advantages of low carbon emissions, low environmental pollution, and utilization of renewable resources. However, the poor interfacial bonding strength and inferior moisture resistance of natural fiber/degradable epoxy composites restrict their application areas. In order to improve the moisture and heat resistance of natural fiber/degradable epoxy resin-based composites, this study modified the surfaces of ramie fibers with hydroxylated carbon nanotubes, silane coupling agents, and sodium hydroxide, respectively. Three types of modified ramie fiber/degradable epoxy composites, namely F-CN-DEP, F-Si-DEP, and F-OH-DEP, were prepared using a winding forming process. The water absorption rate and short-beam shear strength of the materials were tested under different environments, and the fiber morphology and thermal-mechanical properties of the materials were investigated by scanning electron microscopy (SEM) and dynamic mechanical analysis (DMA). The results show that F-CN-DEP exhibited the lowest moisture absorption rate; the highest shear strength, of 43.8 MPa; and a glass transition temperature (Tg) of 121.7 °C. The results demonstrate that carbon nanotubes on the fiber surface can improve the interfacial stability of ramie fiber/degradable epoxy composites in humid and hot environments. These results give guidelines for the development of natural fiber/degradable epoxy composites.

Preparation and Performance Study of Rapid Repair Epoxy Concrete for Bridge Deck Pavement.

With the rapid development of bridge construction, the service life of bridges and traffic volume continue to increase, leading to the gradual appearance of diseases such as potholes and cracks in bridge deck pavements under repeated external loads. These issues severely impact the safety and service life of bridges. The repair of bridge deck potholes and cracks is crucial for ensuring the integrity and safety of bridge structures. Rapid repair materials designed for this purpose play a critical role in effectively and efficiently addressing these issues. In order to address the issues of pavement diseases, this study focuses on the rapid repair of epoxy concrete for bridge deck pavements and its performance is studied using experimental methods. Firstly, carbon black, rubber powder, and other materials were used to improve the elastic modulus and aging resistance of the epoxy concrete. Secondly, the addition of solid asphalt particles provided thermal sensitivity to the repair material. Finally, various properties of the rapid repair epoxy concrete for bridge deck pavements were tested through experiments including compressive strength testing, elastic modulus measurement, thermal sensitivity testing, and anti-UV aging testing. The experimental results show that adding carbon black and rubber powder reduces the elastic modulus of epoxy concrete by 25% compared to normal epoxy concrete, while increasing its aging resistance by 1.8%. The inclusion of solid asphalt particles provided thermal sensitivity to the repair material, contributing to better stress coordination between the repair material and the original pavement material under different temperature conditions. The epoxy concrete has early strength, toughness, and anti-aging properties, making it suitable for rapid repair of bridge deck pavement.

Silica nanoparticles containing nano-silver and chlorhexidine to suppress Porphyromonas gingivalis biofilm and modulate multispecies biofilms toward healthy tendency.

Objectives: This research first investigated the effect of mesoporous silica nanoparticles (nMS) carrying chlorhexidine and silver (nMS-nAg-Chx) on periodontitis-related biofilms. This study aimed to investigate (1) the antibacterial activity on Porphyromonas gingivalis (P. gingivalis) biofilm; (2) the suppressing effect on virulence of P. gingivalis biofilm; (3) the regulating effect on periodontitis-related multispecies biofilm. Methods: Silver nanoparticles (nAg) and chlorhexidine (Chx) were co-loaded into nMS to form nMS-nAg-Chx. Inhibitory zone test and minimum inhibitory concentration (MIC) against P. gingivalis were tested. Growth curves, crystal violet (CV) staining, live/dead staining and scanning electron microscopy (SEM) observation were performed. Biofilm virulence was assessed. The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay and Quantitative Real Time-PCR (qPCR) were performed to validate the activity and composition changes of multispecies biofilm (P. gingivalis, Streptococcus gordonii and Streptococcus sanguinis). Results: nMS-nAg-Chx inhibited P. gingivalis biofilm dose-dependently (p<0.05), with MIC of 18.75 µg/mL. There were fewer live bacteria, less biomass and less virulence in nMS-nAg-Chx groups (p<0.05). nMS-nAg-Chx inhibited and modified periodontitis-related biofilms. The proportion of pathogenic bacteria decreased from 16.08 to 1.07% and that of helpful bacteria increased from 82.65 to 94.31% in 25 µg/mL nMS-nAg-Chx group for 72 h. Conclusions: nMS-nAg-Chx inhibited P. gingivalis growth, decreased biofilm virulence and modulated periodontitis-related multispecies biofilms toward healthy tendency. pH-sensitive nMS-nAg-Chx inhibit the pathogens and regulate oral microecology, showing great potential in periodontitis adjunctive therapy.

Lightweight ZnO/Carbonated Cotton Fiber Nanocomposites for Electromagnetic Interference Applications: Preparation and Properties.

Electromagnetic wave pollution has become a significant harm posed to human health and precision instruments. To shelter such instruments from electromagnetic radiation, high-frequency electromagnetic interference (EMI) shielding materials are extremely desirable. The focus of this research is lightweight, high-absorption EMI shielding composites. Simple aqueous dispersion and drying procedures were used to prepare cotton fiber (CF)-based sheets combined with various zinc oxide (ZnO) contents. These composites were carbonated in a high-temperature furnace at 800 °C for two hours. The obtained CF/ZnO samples have densities of 1.02-1.08 g/cm3. The EMI shielding effectiveness of CF-30% ZnO, CF-50% ZnO, and CF-70% ZnO reached 32.06, 38.08, and 34.69 dB, respectively, to which more than 80% of absorption is attributed. The synergetic effects of carbon networks and surface structures are responsible for the high EMI shielding performance; various reflections inside the interconnected networks may also help in improving their EMI shielding performance.

TRPM2-AS promotes the malignancy of osteosarcoma cells by targeting miR-15b-5p/PPM1D axis.

Osteosarcoma (OS) is a malignant tumor with a low survival rate and a high incidence rate worldwide. Although research has reported the involvement of long non-coding RNAs (lncRNAs) in the pathogenesis of OS cells, the role of TRPM2-AS, miR-15b-5p, and PPM1D in OS progression remains unclear. This study aimed to examine the interaction of the TRPM2-AS/miR-15b-5p/PPM1D axis in OS cells to gain new insights into the molecular mechanism and pathogenesis of OS. After performing in vitro functional assays, we discovered that TRPM2-AS was overexpressed in OS cells. TRPM2-AS silencing impaired OS cell viability, proliferation, and migration, while it induced apoptosis in OS cells in vitro. Our experimental analysis also revealed that PPM1D is a direct target of miR-15b-5p. TRPM2-AS silencing was found to reverse the tumorigenic effect of the miR-15b-5p inhibitor, while the miR-15b-5p inhibitor restored the inhibition of OS caused by silencing PPM1D. Moreover, our findings revealed that miR-15b-5p exerted its tumor-suppressive role by directly targeting PPM1D. In conclusion, this study suggests that TRPM2-AS could promote OS cell malignancy by sponging miR-15b-5p/PPM1D axis.

In Silico Prediction of Metabolic Epoxidation for Drug-like Molecules via Machine Learning Methods.

Epoxidation is one of the reactions in drug metabolism. Since epoxide metabolites would bind with proteins or DNA covalently, drugs should avoid epoxidation metabolism in the body. Due to the instability of epoxide, it is difficult to determine epoxidation experimentally. In silico models based on big data and machine learning methods are hence valuable approaches to predict whether a compound would undergo epoxidation. In this study, we collected 884 epoxidation data manually from various sources, and finally got 829 unique sites of epoxidation. Three types of molecular fingerprints with different lengths (1024, 2048 or 4096 bits) were used to describe the reaction sites. Six machine learning methods were used to build the classification models. The training set and test set were randomly divided into 8 : 2, and 54 models were constructed and evaluated. Four best models were selected for feature selection. The features were then chosen and verified by external validation set. The resulted optimal model had the accuracy and AUC (area under the curve) values at 0.873 and 0.944 for the test set, 0.838 and 0.987 for the external validation set, respectively. The models built in this study could accurately predict whether a compound will undergo epoxidation and which part is most susceptible to epoxidation, which is of great significance for drug design.

Effect of Ginsenoside Rg1 on the intervertebral disc degeneration rats and the degenerative pulposus cells and its mechanism.

OBJECTIVE: To explore the effect of ginsenoside Rg1 on intervertebral disc degeneration (IVDD) in vivo and in vitro and its mechanism. METHODS: 60 rats were underwent surgery to construct rat models of IVDD and divided in the sham group, model group and gradient G-Rg1 groups (10 mg/kg/d, 20 mg/kg/d and 40 mg/kg/d).The change of histology was observed by HE staining, the water content and the expression of ß-catenin in IVD were detected. Rat nucleus pulposus cells(NPCs) were isolated from IVDD rats and divided in D-NPCs group, and gradient G-Rg1 groups(20 µg/ml, 50 µg/ml and 100 µg/ml).The cell proliferation activity, cell apoptosis rate,the expression of proteins related to ECM and Wnt/ß-catenin were detected respectively, Finally the agonist of Wnt/ß-catenin pathway LiCl was used for reversed experiments. RESULTS: In vivo, G-Rg1 treatment could improve the structural disorganization, low water content, NPCs number and aggrecan and collagenⅡ expression in IVD and down-regulate the expression of ß-catenin. In vitro NPCs, G-Rg1 treatment could improve the low cell proliferation, high apoptosis rate and low expression of aggrecan and collagenⅡ in degenerative NPCs in a dose-dependent manner.G-Rg1 treatment could down-regulate the expression of proteins related to ß-catenin signal and LiCl could reverse the increase of cell proliferation and ECM synthesis, decrease of apoptosis of degenerative NPCs induced by G-Rg1. CONCLUSION: G-Rg1 could promote ECM synthesis of degenerative NPCs and inhibiting its apoptosis, improve the IVDD via inhibiting the Wnt/ß-catenin pathway.

Insights into the interaction mechanisms of estrogen-related receptor alpha (ERRα) with ligands via molecular dynamics simulations.

Estrogen-related receptor alpha (ERRα), a member of nuclear receptors (NRs), participates in energy metabolism. Recent experiments identified that several agonists to increase the activity of ERRα, which have a therapeutic effect in improving insulin sensitivity and lowering blood glucose levels. However, the detailed molecular mechanism about how the ligands affect the structure of ERRα remains elusive. To better understand the conformational change of ERRα complexed with agonists and inverse agonists, unbiased molecular dynamics (MD) simulations were performed on the ligand binding domain of ERRα (ERRα-LBD) bound with different ligands. According to the results, the ERRα-agonist interactions were more stable in the presence of the peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α). More importantly, we observed that the binding of inverse agonists would decrease the stability of helix 12 (H12) of ERRα. Moreover, we suggested that Phe232 and Phe414 should be key residues in the interaction pathway from ligands to H12, which provided a possible explanation about how ligands impact the structure of ERRα. These results would provide insights into the design of novel and efficient agonists of ERRα to treat metabolic diabetes.Communicated by Ramaswamy H. Sarma.

A Causal Relationship in Spinal Cord Injury Rat Model Between Microglia Activation and EGFR/MAPK Detected by Overexpression of MicroRNA-325-3p.

Microglial activation and inflammatory response played an important role in the secondary injury of spinal cord injury (SCI). Several microRNAs were associated with this procedure, but the underlying molecular mechanism was poorly understood. Sprague-Dawley (SD) rats were divided into four groups: SCI group (n = 7), agomiR-325-3p group (n = 7), and their control groups. Expression of miR-325-3p and proteins in epidermal growth factor receptor (EGFR)/mitogen-activated protein kinase (MAPK) signaling pathway was evaluated in microglia from SCI rats and primary microglia/BV2 cells activated by lipopolysaccharide (LPS). Concentrations of interleukin-1ß (IL-1ß) and tumor necrosis factor α (TNF-α) in supernatants were measured by ELISA. Low expression of miR-325-3p and activation of EGFR/MAPK was observed in microglia of SCI and LPS-induced primary microglia. Overexpression of miR-325-3p in LPS-induced BV2 cells inhibited microglial activation and release of TNF-α and IL-1ß. Luciferase reporter assay confirmed that miR-325-3p negatively regulated EGFR by targeting its 3'-untranslated regions. Additionally, agomiR-325-3p inhibited the activation of microglia and EGFR/MAPK, alleviating the inflammatory response. These results indicated that miR-325-3p attenuated secondary injury after SCI through inhibition of EGFR/MAPK signaling pathway, the microglial activation, and the release of inflammatory cytokines, suggesting that miR-325-3p may be employed as a therapeutic target for SCI.