Protein S-nitrosylation in the heart tissue has been implicated in several patho (physiological) processes. However, specific protein targets for S-nitrosylation remain largely unknown. In this study, the rat cardiac proteins were incubated in vitro with S-nitrosoglutathione (GSNO), a biologically existing nitric oxide (NO) donor and S-nitrosating agent, to induce protein S-nitrosylation, and the resulting S-nitrosylated proteins were purified by the biotin switch method, followed by two-dimensional gel electrophoresis (2-DE) separation and matrix-assisted laser desorption ionization/time of flight tandem mass spectrometry (MALDI-TOF-MS/MS) identification. Candidate Western blot analysis was also used to identify potential S-nitrosylated proteins. A total of ten proteins including triosephosphate isomerase, glyceraldehyde-3-phosphate dehydrogenase, creatine kinase, adenylate kinase 1 (AK1), enolase 1, destrin, actin, myosin, albumin and Hsp27 were unambiguously identified, among which AK1 was found as a novel target of S-nitrosylation. Further studies showed that AK1 activity in the rat heart extracts was significantly inhibited by GSNO but not oxidized glutathione (GSSG), and the inhibition was completely reversed by dithiothreitol (DTT) post-treatment, demonstrating that S-nitrosylation might serve as a new regulatory mechanism in controlling AK1 activity. This study represents an initial attempt to characterize the S-nitrosoproteome in the heart and highlights the importance of protein S-nitrosylation in cardio function regulation.
To investigate interleukin-1 receptor antagonist (IL-1ra) genotype and its association with the susceptibility of chronic periodontitis in Uighur patients of Xinjiang.Genomic DNA was obtained from buccal swabs of 41 subjects with severe chronic periodontitis (CP), 43 subjects with moderate CP, 49 subjects with mild CP and 92 ethnically matched healthy control individuals. Genotypes of IL-1RN intron 2 VNTR was analyzed by SSP-PCR method. Then compared the differences in distribution of each genotype.A significant over-representation of IL-1RN intron 2 VNTR allele 2 was found in severe chronic periodontitis group.IL-1RN intron 2 VNTR allele 2 may be a risk indicator for the susceptibility of severe chronic periodontitis in Uighur patients of Xinjiang.
AbstractA novel series of N-hydroxy-4-(3-phenylpropanamido)benzamide (HPPB) derivatives comprising N-hydroxybenzamide group as zinc-chelating moiety were designed, synthesized and evaluated as histone deacetylases inhibitors. The thiophene substituted derivative 5j exhibited the best HDAC inhibition activity among these compounds. The present study was designed to evaluate the efficacy of 5j as a candidate compound for cancer therapy. Our results indicated that 5j exhibited better HDAC1, 8 and hela nuclear extract inhibition activities than SAHA, and good antiproliferative activities against a broad spectrum of human cancer cell lines especially for breast cancer. 5j induced cell cycle arrest at G2/M phase, and eventual apoptosis possibly by modulating p21, caspase-3 and Bcl-xL on MDA-MB-231 cells. In addition, 5j down regulated the active form of MMP2, and inhibited the invasion of MDA-MB-231 cell lines. Moreover, 5j significantly delayed the growth of MDA-MB-231 xenografts in mice after 3 weeks of peritoneal injection. In summary, our results suggest that 5j might have therapeutic potential for the treatment of human breast cancer.
Intracerebral hemorrhage (ICH) is one of the most serious complications in patients with chronic kidney disease undergoing long-term hemodialysis. It has high mortality and disability rates and imposes a serious economic burden on the patient's family and society. An early prediction of ICH is essential for timely intervention and improving prognosis. This study aims to build an interpretable machine learning-based model to predict the risk of ICH in patients undergoing hemodialysis.The clinical data of 393 patients with end-stage kidney disease undergoing hemodialysis at three different centers between August 2014 and August 2022 were retrospectively analyzed. A total of 70% of the samples were randomly selected as the training set, and the remaining 30% were used as the validation set. Five machine learning (ML) algorithms, namely, support vector machine (SVM), extreme gradient boosting (XGB), complement Naïve Bayes (CNB), K-nearest neighbor (KNN), and logistic regression (LR), were used to develop a model to predict the risk of ICH in patients with uremia undergoing long-term hemodialysis. In addition, the area under the curve (AUC) values were evaluated to compare the performance of each algorithmic model. Global and individual interpretive analyses of the model were performed using importance ranking and Shapley additive explanations (SHAP) in the training set.A total of 73 patients undergoing hemodialysis developed spontaneous ICH among the 393 patients included in the study. The AUC of SVM, CNB, KNN, LR, and XGB models in the validation dataset were 0.725 (95% CI: 0.610 ~ 0.841), 0.797 (95% CI: 0.690 ~ 0.905), 0.675 (95% CI: 0.560 ~ 0.789), 0.922 (95% CI: 0.862 ~ 0.981), and 0.979 (95% CI: 0.953 ~ 1.000), respectively. Therefore, the XGBoost model had the best performance among the five algorithms. SHAP analysis revealed that the levels of LDL, HDL, CRP, and HGB and pre-hemodialysis blood pressure were the most important factors.The XGB model developed in this study can efficiently predict the risk of a cerebral hemorrhage in patients with uremia undergoing long-term hemodialysis and can help clinicians to make more individualized and rational clinical decisions. ICH events in patients undergoing maintenance hemodialysis (MHD) are associated with serum LDL, HDL, CRP, HGB, and pre-hemodialysis SBP levels.
Introduction: Development of Poly (ADP-ribose) polymerase (PARP) inhibitors has been extensively studied in cancer treatment. Olaparib, the first approved PARP inhibitor, showed potency in the inhibition of both BRCA (breast cancer associated)-mutated and BRCA-unmutated cancers. Methods: Aiming to the discovery of olaparib analogs for the treatment of cancer, structural modifications were performed based on the scaffold of olaparib. In the first series, reduction of carbonyl group to CH2 led to decrease of PARP1 inhibitory activity. Preserving the original carbonyl group, molecules with potent PARP1 inhibitory activities were derived by introduction of hydrazide and aromatic nitrogen mustard groups. The synthesized compounds were evaluated in the in the PARP1 enzyme inhibitory screening, cancer cell based antiproliferative assay, cell cycle arrest and apoptosis studies. Results: It is remarkable that, molecule C2 with chlorambucil substitution, exhibited potent PARP1 inhibitory activity and a broad-spectrum of anticancer potency in the in vitro antiproliferative assay. Compared with olaparib and chlorambucil, molecule C2 also showed significant potency in inhibition of a variety of BRCA-unmutated cell lines. Further analysis revealed the effects of C2 in induction of G2/M phase cell cycle arrest and promotion of apoptosis. Discussion: Collectively, the olaparib-chlorambucil hybrid molecule (C2) could be utilized as a lead compound for further drug design.
A virtual screening approach was performed to develop novel and potent vascular endothelial growth factor receptor 2 inhibitors. The Specs database was filtered by 'rule of five', a pharmacophore model, and docking filter. Sixteen molecules were selected for tube formation assay, a naphthalenol group containing molecule, 12, showed good performance in the study. In the following aortic ring assay and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay, 12 was discovered to efficiently inhibit angiogenesis and tumor cell growth. It is the first time to discover naphthalenol scaffold as potent vascular endothelial growth factor receptor 2 inhibitors. Thus, a molecular dynamic simulation process was applied to discover key features of 12 in binding to vascular endothelial growth factor receptor 2. Hydrophobic interactions were discovered to play significant role in the ligand-receptor binding.
ABSTRACT Background Recently, numerous topical products containing plant‐based ingredients have been reported to resist skin aging. However, there is a lack of sufficient evidence to substantiate these claims. This paper presents a comprehensive review and meta‐analysis to evaluate the efficacy and safety of topical products containing plants or plant extracts in anti‐aging. Methods Four databases—PubMed, Embase, Web of Science, and the Cochrane Library (CENTRAL)—were systematically searched for articles related to plant‐based interventions and skin aging, covering the period from January 2000 to December 2024. A total of eight randomized controlled trials (RCTs) met the inclusion criteria and were included in the meta‐analysis. Results Products containing plant extracts demonstrated a statistically significant difference in improving skin hydration and skin elasticity, reducing melanin and erythema compared to other products. No significant statistical difference was observed in reducing transepidermal water loss (TEWL). Subgroup analysis revealed a significant statistical difference in improvement overall skin elasticity (R2) during short‐term (≤ 8 weeks)treatments, while no statistical difference was observed during long‐term (> 8 weeks)treatments. Additionally, no significant difference was observed in the specific measurements of skin elasticity, including R5 (net elasticity) and R7 (the ratio of elastic recovery to total deformation). Regarding safety, no adverse events were reported in six studies, while the remaining two studies did not specify whether adverse events occurred. Conclusion The meta‐analysis results indicated that botanical preparations significantly enhanced skin hydration, reduced melanin levels and erythema, and increased overall skin elasticity. However, the analysis did not provide sufficient evidence to support a reduction in transepidermal water loss (TEWL), or improvements in R5 (net elasticity) and R7 (the ratio of elastic recovery to total deformation). Systematic Review Registration PROSPERO (york.ac.uk) identifier: CRD42023478803
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