Oxidative DNA and RNA damage and their prognostic values during Salmonella enteritidis-induced intestinal infection in rats

Abstract

Emerging evidence suggests that microbial pathogens may induce oxidative stress in infected hosts. The aim of the present study was to investigate the relationship between changes in oxidative stress and intestinal infection with and without antibiotic treatment in animal models. Sprague-Dawley (SD) rats were divided into three groups: rats infected with Salmonella enterica serovar Enteritidis (S. enteritidis), rats infected with S. enteritidis followed by norfloxacin treatment, and the control group. To evaluate oxidative stress changes, levels of 8-oxo-7,8-dihydroguanosine (8-oxo-Gsn) and 8-oxo-7,8-dihydro-2-deoxyguanosine (8-oxo-dGsn), which represented oxidative damage to RNA and DNA, respectively, were analysed in urine and tissue samples. In urine, the level of 8-oxo-Gsn increased significantly after oral exposure to S. enteritidis (p?≤?0.001) and returned to baseline after recovery. Notably, norfloxacin treatment decreased the level of 8-oxo-Gsn in urine significantly (p?=?0.001). Changes of 8-oxo-Gsn measured in tissues from the small intestine, colon, liver and spleen were consistent with 8-oxo-Gsn measured in urine. Our study suggested that 8-oxo-Gsn in urine may serve as a highly sensitive biomarker for evaluating the severity of S. enteritidis infection and the effectiveness of antibiotic treatment against infection.     

Hydrogen sulfide alleviates uranium-induced kidney cell apoptosis mediated by ER stress via 20S proteasome involving in Akt/GSK-3β/Fyn-Nrf2 signaling

Abstract

Hydrogen sulfide (H₂S) shows antioxidative, anti-inflammatory, antiapoptotic, and cytoprotective effects in kidneys. Recently, H₂S has been reported to alleviate uranium-induced rat nephrotoxicity through oxidative stress and inflammatory response via Nrf2-NF-κB pathways. Here, the protective effect and molecular mechanism of H₂S on uranium-induced apoptosis were examined in normal rat kidney proximal cells (NRK-52^E) in vitro. The results indicate that NaHS (an H₂S donor) administration in uranium-intoxicated kidney cells ameliorated uranium-induced reactive oxygen species generation, caspase-3-dependent apoptosis, and endoplasmic reticulum (ER) stress identified through several key markers including GRP78, C/EBP homologous protein (CHOP), and caspase-12. NaHS treatment in uranium-intoxicated kidney cells abolished the effects of uranium on Akt phosphorylation, GSK-3β activation, increased Fyn nuclear expression, and concomitantly decreased Nrf2 nuclear expression. NaHS administration in uranium-treated kidney cells resorted uranium-decreased the expression of two key subunit PSMA6 and PSMB7 in 20S proteasome. But, DRB (an Nrf2 inhibitor) administration abrogated the effects of NaHS on PSMA6 and PSMB7 expression in uranium-contaminated kidney cells. Bortezomib (a proteasome inhibitor) treatment in NaHS pulsing uranium cotreated kidney cells reversed the effects of NaHS on not only PSMA6 and PSMB7 but also GRP78 and CHOP. Taken together, all data suggest that H₂S can attenuate uranium-induced kidney cell apoptosis mediated by ER stress via 20S proteasome involving in Akt/GSK-3β/Fyn-Nrf2 signaling axis.     

Changes in arbuscular mycorrhizal fungus community along an exotic plant Eupatorium adenophorum invasion in a chinese secondary forest

Knowledge of the changes in arbuscular mycorrhizal (AM) fungi is fundamental for understanding the success of exotic plant invasions in natural ecosystems. In this study, AM fungal colonization and spore community were examined along an invasive gradient of the exotic plant Eupatorium adenophorum in a secondary forest in southwestern China. With increasing E. adenophorum invasion, the density of arbuscules in the roots of E. adenophorum significantly increased, but the AM root colonization rate and the densities of vesicles and hyphal coils in roots of E. adenophorum were not significantly different. A total of 29 AM fungi belonging to nine genera were identified based on spore morphology. Claroideoglomus etunicatum, Funneliformis geosporus, and Glomus aggregatum were the most common AM fungal species. The E. adenophorum invasion significantly decreased the AM fungal spore density in the soil. Furthermore, with increasing of E. adenophorum invasion the spore densities of C. etunicatum, G. aggregatum, and G. arenarium significantly decreased, whereas F. geosporus significantly increased. Nonmetric multidimensional scaling demonstrated that the AM fungus community composition was significantly different (P=0.003) in the different invasive levels of E. adenophorum, and significantly correlated with plant species richness, soil total P, and soil NO₃ ^?-N. The results suggest that the alteration in AM fungus community might be caused by E. adenophorum invasion via changing the local plant community and soil properties in a Chinese secondary forest ecosystem.     

Comprehensive annotation of microRNA expression profiles

Background

MicroRNAs (miRNAs) regulate many biological processes by post-translational gene silencing. Analysis of miRNA expression profiles is a reliable method for investigating particular biological processes due to the stability of miRNA and the development of advanced sequencing methods. However, this approach is limited by the broad specificity of miRNAs, which may target several mRNAs.

Result

In this study, we developed a method for comprehensive annotation of miRNA array or deep sequencing data for investigation of cellular biological effects. Using this method, the specific pathways and biological processes involved in Alzheimer’s disease were predicted with high correlation in four independent samples. Furthermore, this method was validated for evaluation of cadmium telluride (CdTe) nanomaterial cytotoxicity. As a result, apoptosis pathways were selected as the top pathways associated with CdTe nanoparticle exposure, which is consistent with previous studies.

Conclusions

Our findings contribute to the validation of miRNA microarray or deep sequencing results for early diagnosis of disease and evaluation of the biological safety of new materials and drugs.

     

A conservation and rigidity based method for detecting critical protein residues

Background

Certain amino acids in proteins play a critical role in determining their structural stability and function. Examples include flexible regions such as hinges which allow domain motion, and highly conserved residues on functional interfaces which allow interactions with other proteins. Detecting these regions can aid in the analysis and simulation of protein rigidity and conformational changes, and helps characterizing protein binding and docking. We present an analysis of critical residues in proteins using a combination of two complementary techniques. One method performs in-silico mutations and analyzes the protein's rigidity to infer the role of a point substitution to Glycine or Alanine. The other method uses evolutionary conservation to find functional interfaces in proteins.

Results

We applied the two methods to a dataset of proteins, including biomolecules with experimentally known critical residues as determined by the free energy of unfolding. Our results show that the combination of the two methods can detect the vast majority of critical residues in tested proteins.

Conclusions

Our results show that the combination of the two methods has the potential to detect more information than each method separately. Future work will provide a confidence level for the criticalness of a residue to improve the accuracy of our method and eliminate false positives. Once the combined methods are integrated into one scoring function, it can be applied to other domains such as estimating functional interfaces.

     

A conservation and biophysics guided stochastic approach to refining docked multimeric proteins

Background

We introduce a protein docking refinement method that accepts complexes consisting of any number of monomeric units. The method uses a scoring function based on a tight coupling between evolutionary conservation, geometry and physico-chemical interactions. Understanding the role of protein complexes in the basic biology of organisms heavily relies on the detection of protein complexes and their structures. Different computational docking methods are developed for this purpose, however, these methods are often not accurate and their results need to be further refined to improve the geometry and the energy of the resulting complexes. Also, despite the fact that complexes in nature often have more than two monomers, most docking methods focus on dimers since the computational complexity increases exponentially due to the addition of monomeric units.

Results

Our results show that the refinement scheme can efficiently handle complexes with more than two monomers by biasing the results towards complexes with native interactions, filtering out false positive results. Our refined complexes have better IRMSDs with respect to the known complexes and lower energies than those initial docked structures.

Conclusions

Evolutionary conservation information allows us to bias our results towards possible functional interfaces, and the probabilistic selection scheme helps us to escape local energy minima. We aim to incorporate our refinement method in a larger framework which also enables docking of multimeric complexes given only monomeric structures.

     

Statins in cardiometabolic disease: what makes pitavastatin different?

The term cardiometabolic disease encompasses a range of lifestyle-related conditions, including Metabolic syndrome (MetS) and type 2 diabetes (T2D), that are characterized by different combinations of cardiovascular (CV) risk factors, including dyslipidemia, abdominal obesity, hypertension, hyperglycemia/insulin resistance, and vascular inflammation. These risk factors individually and interdependently increase the risk of CV and cerebrovascular events, and represent one of the biggest health challenges worldwide today. CV diseases account for almost 50% of all deaths in Europe and around 30% of all deaths worldwide. Furthermore, the risk of CV death is increased twofold to fourfold in people with T2D. Whilst the clinical management of CV disease has improved in Western Europe, the pandemic of obesity and T2D reduces the impact of these gains. This, together with the growing, aging population, means the number of CV deaths is predicted to increase from 17.1 million worldwide in 2004 to 23.6 million in 2030. The recommended treatment for MetS is lifestyle change followed by treatment for the individual risk factors. Numerous studies have shown that lowering low-density lipoprotein-cholesterol (LDL-C) levels using statins can significantly reduce CV risk in people with and without T2D or MetS. However, the risk of major vascular events in those attaining the maximum levels of LDL-C-reduction is only reduced by around one-third, which leaves substantial residual risk. Recent studies suggest that low high-density lipoprotein-cholesterol (HDL-C) (<1 .0 mmol/l; 40 mg/dl) and high triglyceride levels (≥1.7 mmol/l; 150 mg/dl) are independent risk factors for CV disease and that the relationship between HDL-C and CV risk persists even when on-treatment LDL-C levels are low (<1.7 mmol/l; 70 mg/dl). European guidelines highlight the importance of reducing residual risk by targeting these risk factors in addition to LDL-C. This is particularly important in patients with T2D and MetS because obesity and high levels of glycated hemoglobin are directly related to low levels of HDL-C and high triglyceride. Although most statins have a similar low-density lipoprotein-lowering efficacy, differences in chemical structure and pharmacokinetic profile can lead to variations in pleiotropic effects (for example, high-density lipoprotein-elevating efficacy), adverse event profiles, and drug-drug interactions. The choice of statin should therefore depend on the needs of the individual patient. The following reviews will discuss the potential benefits of pitavastatin versus other statins in the treatment of patients with dyslipidemia and MetS or T2D, focusing on its effects on HDL-C quantity and quality, its potential impact on atherosclerosis and CV risk, and its metabolic characteristics that reduce the risk of drug interactions. Recent controversies surrounding the potentially diabetogenic effects of statins will also be discussed.     

Design,synthesis, and biological evaluation of novel substituted thiourea derivatives as potential anticancer agents for NSCLC by blocking K-Ras protein-effectors interactions

Abstract

Mutation of the proto-oncogene K-Ras is one of the most common molecular mechanisms in non-small cell lung cancer. Many drugs for treating lung cancer have been developed, however, due to clinical observed K-Ras mutations, corresponding chemotherapy and targeted therapy for such mutation are not efficient enough. In this study, on the basis of the crystal structure of K-Ras, 21 analogues (TKR01–TKR21) containing urea or thiourea were rationally designed, which can effectively inhibit the lung cancer cell A549 growth. The designing of these compounds was based on the structure of K-Ras protein, and the related groups were replaced by bioisosteres to improve the affinity and selectivity. Biological testing revealed that compound TKR15 could significantly inhibit the proliferation of A549 cell with IC₅₀ of 0.21?µM. Docking analysis showed that the TKR15 can effectively bind to the hydrophobic cavity and form a hydrogen bond with the Glu37. In addition, through flow apoptosis assay and immunofluorescence staining assay, it confirmed that this compound can inhibit A549 cell proliferation with the mechanism of blocking K-Ras^G12V protein and effector proteins interactions through the apoptotic pathway. In conclusion, our studies in finding novel potent compound (TKR15) with confirmed mechanism showed great potential for further optimisation and other medicinal chemistry relevant studies.     

Predicting beta-turns in proteins using support vector machines with fractional polynomials

Background

β-turns are secondary structure type that have essential role in molecular recognition, protein folding, and stability. They are found to be the most common type of non-repetitive structures since 25% of amino acids in protein structures are situated on them. Their prediction is considered to be one of the crucial problems in bioinformatics and molecular biology, which can provide valuable insights and inputs for the fold recognition and drug design.

Results

We propose an approach that combines support vector machines (SVMs) and logistic regression (LR) in a hybrid prediction method, which we call (H-SVM-LR) to predict β-turns in proteins. Fractional polynomials are used for LR modeling. We utilize position specific scoring matrices (PSSMs) and predicted secondary structure (PSS) as features. Our simulation studies show that H-SVM-LR achieves Qtotal of 82.87%, 82.84%, and 82.32% on the BT426, BT547, and BT823 datasets respectively. These values are the highest among other β-turns prediction methods that are based on PSSMs and secondary structure information. H-SVM-LR also achieves favorable performance in predicting β-turns as measured by the Matthew's correlation coefficient (MCC) on these datasets. Furthermore, H-SVM-LR shows good performance when considering shape strings as additional features.

Conclusions

In this paper, we present a comprehensive approach for β-turns prediction. Experiments show that our proposed approach achieves better performance compared to other competing prediction methods.