Sensitivity of Prestaining RNA with Ethidium Bromide Before Electrophoresis and Performance of Subsequent Northern Blots Using Heterologous DNA Probes

Adding ethidium bromide (EtBr) at low concentrations to RNA samples before running formaldehyde–agarose gels affords the advantages of checking RNA integrity and evaluating the quality of size-separation at any time during electrophoresis or immediately after either electrophoresis or blotted the separated RNA onto the membrane without significantly compromising mobility, transfer, or hybridization. In this study, we systematically examined the factors that affect the sensitivity of RNA prestaining by heating RNA samples that include EtBr before electrophoresis under different denaturation conditions. We also examined the efficiency of the hybridization of EtBr-prestained RNA with heterologous DNA probes. The results showed that the fluorescent intensity of EtBr-prestained RNA was affected not only by the EtBr concentration as previously reported but also by the RNA amount, denaturation time, and denaturation temperature. Prior staining of RNA with 40 μg/mL EtBr significantly decreased the efficiency of Northern blot hybridization with heterologous DNA probes. We propose that to best combine staining sensitivity and the efficiency of Northern blot hybridization with heterologous DNA probes, the concentration of EtBr used to prestain RNA should not exceed 30 μg/mL. The efficiency of the hybridization of EtBr-prestained RNA was affected not only by factors that affect staining sensitivity but also by the type of probe used.     

Comparison of Rapid DNA Extraction Methods Applied to PCR Identification of Medicinal Mushroom Ganoderma spp.

Abstract

Four different DNA extraction methods were used to extract genomic DNA of the medicinal mushroom Lingzhi from its developing stage materials, such as mycelium, dry fruiting body, or sliced and spore powder or sporoderm‐broken spore powder. The DNA samples were analyzed using agarose gel electrophoresis, UV spectrophotometer, and PCR amplification. According to the average yields and purity of DNA, high salt concentrations and low pH methods were the best for DNA extraction. The mycelia and sporoderm‐broken spore powder yielded higher and purer DNA. The method developed could effectively eliminate the influence of the secondary metabolites to DNA extraction. The DNA samples extracted from the developed method could be successfully used for PCR applications.     

Expression,purification, and characterization of N-terminal His-tagged proteins with mutations in zinc finger 3 of zinc finger protein ZNF191(243–368)

Abstract

Zinc finger protein ZNF191(243–368), the zinc finger region of ZNF191, is potentially associated with cell proliferation in hepatocellular carninoma. A His-tag expression system was used to express and purify proteins with mutations in the zinc finger 3 of ZNF191(243–368) for analysis of protein properties, structure, and functions. The purification of the His-tag fusion proteins was simpler and faster than that of the ZNF191(243–368) inclusion bodies. The properties and structures of the His-tag fusion mutant proteins were investigated using spectrographic techniques and DNA hydrolysis experiment. The His₆-tag system could be used to express ZNF191(243–368). The presence of the His₆-tag at the N-terminus of ZNF191(243–368) did not evidently affect its properties and structure. However, the site-directed mutations in zinc finger 3 affected the structure of the protein. The DNA hydrolase activity of His₆-ZF-F3/H4 suggested that four histidines in zinc finger 3 might form a structure similar to that of the active center in a hydrolase. This work reports that continuous histidines need to form a certain structure for specific functions, and provides new insights into the design of an artificial nuclease.     

Preparation and evaluation of a peptide nucleic acid gene chip system associated with surface plasmon resonance

The aim of this study was to construct a gene chip system based on a surface plasmon resonance technique, where peptide nucleic acid (PNA) oligomers are used as probes. Since the self-assembled monolayer (SAM) technology offers good control at the molecular level, we prepared 2D surface chemistry via SAM for probe attachments. PNA, which was designed according to the bioinformatics, was immobilized on the SAM-modified chip, and subsequently, relevant parameters of the experiment were ensured and optimized. Our results suggest that the ion strength and pH value of the buffer solution do not play significant roles in PNA or its complementary strand hybridization. The PNA probe binds to its complementary nucleic acid strand with a higher sensitivity and specificity compared to those of a traditional DNA probe. The PNA probe combined with surface plasmon resonance (SPR) technology has the benefits of being a label-free and in-real time monitor, as well as having improved hybridization and stability efficiency, which highlight the PNA gene chip detection system as a promising biosensor for clinical applications.     

Live/dead state is not the factor influencing adhesion ability of Bifidobacterium animalis KLDS2.0603

Two essential requirements for probiotic bifidobacteria are that they be “live” and have “colonization” ability, following FAO/WHO guideline recommendations. The amount of research on the adhesion ability of bifidobacteria compares poorly with that of other probiotic bacteria, such as lactobacilli. The aim of the present study was to determine how gastrointestinal conditions affect the adhesion ability of bifidobacteria, and to investigate the relationship between the adhesion ability and the live/dead state of bifidobacteria. The adhesion ability of Bifidobacterium animalis KLDS2.0603 that had been subjected to the digestive enzymes, pepsin, trypsin, and proteinase K, was decreased significantly, but these treatments did not significantly change the strain’s survival rates, which were 98.78%, 97.60%, and 97.63% respectively. B. animalis KLDS2.0603 subjected to LiCl retained its adhesion ability but had a lower survival rate (59.28%) than the control group (P<0.01). B. animalis KLDS 2.0603 subjected to sodium metaperiodate exhibited higher adhesion ability than the control group (P<0.01), but the bacterial cells were killed totally. The results of transmission electron microscopy and laser scanning confocal microscopy showed that live/dead state of bifidobacteria was not one of the main factors that affected the adhesion ability of bifidobacteira, and that the substances affecting the adhesion ability of bifidobacteria were on the outer surface layer of the bifidobacterial cells. Our results also indicated that the substances related to the adhesion ability of bifidobacteria are proteinaceous. The above results will help us to understand the adhesion and colonization processes of bifidobacteria in the human gastrointestinal tract.     

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.     

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.