NADPHnet: a novel strategy to predict compounds for regulation of NADPH metabolism via network-based methods.

Identification of compounds to modulate NADPH metabolism is crucial for understanding complex diseases and developing effective therapies. However, the complex nature of NADPH metabolism poses challenges in achieving this goal. In this study, we proposed a novel strategy named NADPHnet to predict key proteins and drug-target interactions related to NADPH metabolism via network-based methods. Different from traditional approaches only focusing on one single protein, NADPHnet could screen compounds to modulate NADPH metabolism from a comprehensive view. Specifically, NADPHnet identified key proteins involved in regulation of NADPH metabolism using network-based methods, and characterized the impact of natural products on NADPH metabolism using a combined score, NADPH-Score. NADPHnet demonstrated a broader applicability domain and improved accuracy in the external validation set. This approach was further employed along with molecular docking to identify 27 compounds from a natural product library, 6 of which exhibited concentration-dependent changes of cellular NADPH level within 100 µM, with Oxyberberine showing promising effects even at 10 µM. Mechanistic and pathological analyses of Oxyberberine suggest potential novel mechanisms to affect diabetes and cancer. Overall, NADPHnet offers a promising method for prediction of NADPH metabolism modulation and advances drug discovery for complex diseases.

A Novel Mitochondrial-Related Gene Signature for the Tumor Immune Microenvironment Evaluation and Prognosis Prediction in Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) remains the most common deadly disease and has a poor prognosis. More and more studies have reported that mitochondrial-related genes (MTRGs) were associated with the clinical outcomes of multiple tumors solely. In this study, we aimed to develop a novel prognostic model based on MTRGs. Differentially expressed MTRGs were identified from TCGA-LUAD and GSE31210 cohorts. Univariate Cox regression analysis was utilized to screen differentially expressed MTRGs that were related to prognosis of LUAD. Then, LASSO Cox regression analysis was used to develop a prognostic signature. ESTIMATE was used for estimating the fractions of immune cell types. In this study, we identified 44 overlapping differentially expressed MTRGs in TCGA-LUAD and GSE31210 cohorts. Among 44 overlapping differentially expressed MTRGs, nine genes were associated with prognosis of LUAD. When the penalty parameter lambda was the minimum, there were six genes meeting the conditions of constructing the signature, including SERPINB5, CCNB1, FGR MAOB, SH3BP5, and CYP24A1. The survival analysis suggested that prognosis of patients in the high-risk group was significantly worse than that in the low-risk group. Cox regression analyses showed that the risk score was an independent predictor of LUAD prognosis. As with the results of ESTIMATE score, the degree of immune cell infiltration in the low-risk group was higher than that in the high-risk group, such as TIL, Treg, and B cells. In addition, TMB and cancer stem cell infiltration were higher in the low-risk group than the high-risk group. In conclusion, we developed a novel MTRG signature acting as a negative independent prognostic factor. In the future, individualized treatments and medical decision-making may benefit from using the predicted model.

[Progress in study of selective ERß ligands].

Estrogen receptors (ERs) are members of nuclear receptors and related to several diseases such as cancer, inflammation and osteoporosis. ERs have two forms, ERα and ERß, which have different functions and organism distributions. Compounds selectively targeting ERß can regulate important physiological functions and avoid the side effects caused by targeting ERα. Therefore, selective ERß ligands have received considerable research interest in recent years. In this article, different kinds of selective ERß ligands were summarized and their structure-activity relationships were also analyzed.

Selective ligands of estrogen receptor ß discovered using pharmacophore mapping and structure-based virtual screening.

AIM: To discover novel ligands of estrogen receptor (ER) ß using pharmacophore mapping and structure-based screening. METHODS: A computer-aided strategy combining pharmacophore mapping and structure-based screening was used to screen the Maybridge and Enamine databases. Yeast two-hybrid (Y2H) assay was used to detect the activity and selectivity of the chosen compounds. The transcriptional activities of the chosen compounds were demonstrated with luciferase reporter assays. The anti-proliferative effects of ER antagonists against MCF-7 and MDA-MB-231 breast cancer cells were examined using MTT assay, and the mechanisms of action were analyzed with flow cytometry analysis and Western blotting. RESULTS: Through in silico screen, 95 compounds were chosen for testing in Y2H assay, which led to 20 potent ligands, including 10 agonists, 8 antagonists and 2 partial agonists with EC50 or IC50 values at µmol/L. Furthermore, 6 agonists exhibited absolute selectivity for ERß, and 3 agonists showed higher selectivity for ERß. The agonists 1g and 1h (10, 25, and 50 µmol/L) dose-dependently increased ER transcriptional activities, whereas the antagonists 2a and 2d (10, 25, and 50 µmol/L) caused dose-dependent inhibition on the activities. The antagonists and partial agonists at 100 µmol/L suppressed the proliferation of ERα positive MCF-7 cells and ERß positive MDA-MB-231 cells, but were more effective against MDA-MB-231 cells. Treatment of MDA-MB-231 cells with antagonists 2a and 2d (25 and 50 µmol/L) dose-dependently increased the population of cells in the S phase. Both 2a and 2d treatment dose-dependently decreased the expression levels of cyclin A and CDK2. Meanwhile, the downregulation of cyclin E was only caused by 2d, while 2a treatment did not cause significant changes in the protein levels of cyclin E. CONCLUSION: The selective ligands discovered in this study are promising drug candidates to be used as molecular probes to explore the differences between ERα and ERß.

Structure-based ensemble-QSAR model: a novel approach to the study of the EGFR tyrosine kinase and its inhibitors.

AIM: To develop a novel 3D-QSAR approach for study of the epidermal growth factor receptor tyrosine kinase (EGFR TK) and its inhibitors. METHODS: One hundred thirty nine EGFR TK inhibitors were classified into 3 clusters. Ensemble docking of these inhibitors with 19 EGFR TK crystal structures was performed. Three protein structures that showed the best recognition of each cluster were selected based on the docking results. Then, a novel QSAR (ensemble-QSAR) building method was developed based on the ligand conformations determined by the corresponding protein structures. RESULTS: Compared with the 3D-QSAR model, in which the ligand conformations were determined by a single protein structure, ensemble-QSAR exhibited higher R2 (0.87) and Q2 (0.78) values and thus appeared to be a more reliable and better predictive model. Ensemble-QSAR was also able to more accurately describe the interactions between the target and the ligands. CONCLUSION: The novel ensemble-QSAR model built in this study outperforms the traditional 3D-QSAR model in rationality, and provides a good example of selecting suitable protein structures for docking prediction and for building structure-based QSAR using available protein structures.

[Progress in study of the structure, catalytic mechanism and inhibitors of aromatase].

Aromatase is a key enzyme responsible for in vivo estrogen biosynthesis. Inhibition of the activity of the aromatase has become an alterative way for treatment of breast cancer. In this review, the structure and catalytic mechanism of the aromatase is briefly introduced followed by thorough review of the progress in the study of the steroidal and non-steroidal aromatase inhibitors. This review is focused on the natural compounds that exhibit the aromatase inhibition, which include flavonoids, xanthones, coumarins, and sesquiterpenes. The structure-activity relationship of these compounds is also discussed.

[Construction of the recombinant adenovirus expressing the CVB3 sVP1-C3d3 protein and study on its immunological effects in mice].

AIM: To construct recombinant adenovirus Ad/C3d3-sVP1 and investigate the immune effects against coxsackievirus infection in mouse. METHODS: The recombinant adenovirus Ad/sVP1-C3d3 was constructed and packaged. BALB/c mouse were divided into four groups: Ad/sVP1-C3d3 group, Ad/VP1 group, Ad group and PBS group. The mice in each group were immunized by intramuscular injection. The titers of sera IgG and neutralizing antibody were detected by ELISA method and trace neutralization assay, respectively.The specific CTL cytotoxic activity was detected by CCK-8 assay. The mice in each group were challenged with lethal dose of coxsackievirus, the titers of the sera virus were titrated. RESULTS: The recombinant adenovirus Ad/sVP1-C3d3 was successfully constructed. It's observed that the titers of CVB3 VP1 specific antibody and neutralizing antibody were much higher than those of the other three groups(P<0.01). CTL cytotoxicity activities was much higher than PBS and Ad group(P<0.01), but little higher than Ad/VP1 group(P<0.05).The titer of sera virus was lower than Ad and PBS groups after CVB3 challenged(P<0.05). CONCLUSION: Both the celluar and humoral immune responses in mice could been significantly enhanced by Ad/sVP1-C3d3.

Bioassay of estrogenic activity of effluent and influent in a farm wastewater treatment plant using an in vitro recombinant assay with yeast cells.

OBJECTIVE: Environmental estrogens at an elevated concentration are known to produce adverse effects on human and animal life. However, the majority of researches have been focused on industrial discharges, while the impact of livestock wastes as a source of endocrine disrupters in aquatic environments has been rarely elucidated. In order to investigate the contribution of environmental estrogens from livestock, the estrogenic activity in water samples from a farm wastewater treatment plant was analyzed by a recombinant yeast screening method. METHODS: The extracts prepared from 15 selected water samples from the farm wastewater treatment plant, among which 6 samples were from pre-treatment section (influents) and 9 from post-treatment section (effluents), were analyzed for estrogenic activity by cellar bioassay. Yeast cells transfected with the expression plasmid of human estrogen receptor and the Lac Z reporter plasmid encoding beta-galactossidase, were used to measure the estrogen-like compounds in the farm wastewater treatment plant. RESULTS: The wastewater samples from influents showed a higher estrogenic potency than the effluent samples showing a low induction of beta-galactossidase relative to solvent control condition. By comparison with a standard curve for 17 beta-estradiol (E2), estrogenic potency in water samples from the influents was calculated as E2-equivalent and ranged from 0.1 to 150 pM E2-equivalent. The estrogenic potency in water samples from the effluents was significantly lower than that in the influents, and 7 water samples had less detectable limit in the total of 9 samples. CONCLUSION: Yeast bioassay of estrogenic activity in most of the samples from the farm wastewater after disposal by traditional sewage treatment showed negative results.