Can Carnosine Prevent the Aging-Induced Changes of Blood Platelet and Brain Regional Monoamine Oxidase-A mRNA in Relation to its Activity?

It is well known that the monoamine oxidase (MAO) activity deregulates during aging along with anti-oxidant activity. Carnosine (β-Ala-l-His) is an endogenous dipeptide biomolecule, having both anti-oxidant and anti-glycating properties. The present study deals with the effect of carnosine on aging-induced changes in MAO-A mRNA expression of brain regions and blood platelets in relation to their MAO-A activity. Results showed that aging significantly and characteristically increased the brain regional MAO-A mRNA whereas, in blood platelets it was significantly reduced with an increase in blood platelet counts. Carnosine attenuated both aging-induced (i) increase in brain regional MAO-A mRNA expression and blood platelet count, (ii) decrease in blood platelet MAO-A mRNA expression and its (platelet MAO-A) activity without affecting the young rats’ brain regions and platelet. The present results thus suggest that carnosine attenuated and restored the aging-induced (a) increase of platelet count and (b) changes in brain regional and blood platelet MAO-A mRNA expression and (c) decrease in platelet MAO-A activity, towards their respective basal level that were observed in young rats.     

Novel Dipeptidyl Peptidase IV Inhibitory and Antioxidant Peptides Derived from Human Gastrointestinal Endogenous Proteins

Human gastrointestinal endogenous proteins (GEP) include the proteins mucins, serum albumin, digestive enzymes, and proteins from sloughed epithelial and microbial-cells. GEP play a vital role in the digestion of food, but are also simultaneously digested by proteases and peptidases of the gastrointestinal tract (GIT). Recent studies suggest that during gastrointestinal digestion, similar to dietary proteins, GEP may also give rise to bioactive peptides. In the present study, the protein sequences of 11 representative GEP were subjected to simulated in silico GIT (SIGIT) digestion. Following SIGIT digestion, 19 novel GEP-derived peptide sequences were selected using quantitative structure activity relationship rules for chemical synthesis. The peptides were then tested for their in vitro dipeptidyl peptidase IV (DPP-IV) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) inhibition, and for their ferric reducing antioxidant power (FRAP). Two novel DPP-IV inhibitory peptides with the amino acid sequences RPCF (IC₅₀ = 800.51 ± 49.00 µM) and MIM (IC₅₀ = 1056.78 ± 61.11 µM), and five novel antioxidant peptides CCK, RPCF, CRPK, QQCP and DCR were identified. The results of this study indicate that GEP are a significant source of bioactive peptides with potential novel bioactive peptide fragments within their sequences.     

Interleukin 6 and/or Interleukin 17A Modulate the OPG/RANKL System of MC3T3-E1 Murine Osteoblast Cell Line

Cytokines such as interleukin-6 (IL-6) and IL-17 which act as key regulators of the immune response have been identified to have a potential role in the bone remodeling mechanism. Receptor activator of NF-κB ligand (RANKL) has been shown to regulate osteoclast differentiation and function while the osteoprotegerin (OPG) blocks the binding of RANKL and inhibits the differentiation of osteoclasts, thus favoring osteogenesis. Alkaline phosphatase (ALP) on the other hand works as early mineralization indicator in bone regulation. The current study aims to determine the potential role of IL-6 and IL-17A in regulating the OPG/RANKL system of the murine osteoblast cell line (MC3T3-E1). Gene expression analysis showed significant up-regulation of OPG and ALP by all the treated groups (rIL-6, rIL-17A and rIL-6 + rIL-17A). In contrast, treatment of cells with rIL-6 and/or rIL-17A showed down-regulation of RANKL expression. Interestingly, the osteoblast cells treated with combinations of rIL-6 + rIL17A showed marked increased in OPG/RANKL ratio. Similar pattern of protein expression was observed in the osteoblasts treated with rIL-6 and/or rIL-17A as detected by western blotting and ELISA. These findings suggest a new mechanism of regulation by these cytokines on the expression of OPG and RANKL, which could promote osteogenesis and diminish osteoclastogenesis.     

Improving the iterative Linear Interaction Energy approach using automated recognition of configurational transitions

Recently an iterative method was proposed to enhance the accuracy and efficiency of ligand-protein binding affinity prediction through linear interaction energy (LIE) theory. For ligand binding to flexible Cytochrome P450s (CYPs), this method was shown to decrease the root-mean-square error and standard deviation of error prediction by combining interaction energies of simulations starting from different conformations. Thereby, different parts of protein-ligand conformational space are sampled in parallel simulations. The iterative LIE framework relies on the assumption that separate simulations explore different local parts of phase space, and do not show transitions to other parts of configurational space that are already covered in parallel simulations. In this work, a method is proposed to (automatically) detect such transitions during the simulations that are performed to construct LIE models and to predict binding affinities. Using noise-canceling techniques and splines to fit time series of the raw data for the interaction energies, transitions during simulation between different parts of phase space are identified. Boolean selection criteria are then applied to determine which parts of the interaction energy trajectories are to be used as input for the LIE calculations. Here we show that this filtering approach benefits the predictive quality of our previous CYP 2D6-aryloxypropanolamine LIE model. In addition, an analysis is performed of the gain in computational efficiency that can be obtained from monitoring simulations using the proposed filtering method and by prematurely terminating simulations accordingly.     

Molecular dynamics simulations of a lithium/sodium carbonate mixture

The diffusion and ionic conductivity of Li _x Na_1?x CO₃ salt mixtures were studied by means of Molecular Dynamics (MD) simulations, using the Janssen and Tissen model (Janssen and Tissen, Mol Simul 5:83–98; 1990). These salts have received particular attention due to their central role in fuel cells technology, and reliable numerical methods that could perform as important interpretative tool of experimental data are thus required but still lacking. The chosen computational model nicely reproduces the main structural behaviour of the pure Li₂CO₃, Na₂CO₃ and K₂CO₃ carbonates, but also of their Li/K and Li/Na mixtures. However, it fails to accurately describe dynamic properties such as activation energies of diffusion and conduction processes, outlining the need to develop more accurate models for the simulation of molten salt carbonates.     

Generalized logical model based on network topology to capture the dynamical trends of cellular signaling pathways

Background

Cellular responses to extracellular perturbations require signaling pathways to capture and transmit the signals. However, the underlying molecular mechanisms of signal transduction are not yet fully understood, thus detailed and comprehensive models may not be available for all the signaling pathways. In particular, insufficient knowledge of parameters, which is a long-standing hindrance for quantitative kinetic modeling necessitates the use of parameter-free methods for modeling and simulation to capture dynamic properties of signaling pathways.

Results

We present a computational model that is able to simulate the graded responses to degradations, the sigmoidal biological relationships between signaling molecules and the effects of scheduled perturbations to the cells. The simulation results are validated using experimental data of protein phosphorylation, demonstrating that the proposed model is capable of capturing the main trend of protein activities during the process of signal transduction. Compared with existing simulators, our model has better performance on predicting the state transitions of signaling networks.

Conclusion

The proposed simulation tool provides a valuable resource for modeling cellular signaling pathways using a knowledge-based method.

     

Prediction of drugs having opposite effects on disease genes in a directed network

Background

Developing novel uses of approved drugs, called drug repositioning, can reduce costs and times in traditional drug development. Network-based approaches have presented promising results in this field. However, even though various types of interactions such as activation or inhibition exist in drug-target interactions and molecular pathways, most of previous network-based studies disregarded this information.

Methods

We developed a novel computational method, Prediction of Drugs having Opposite effects on Disease genes (PDOD), for identifying drugs having opposite effects on altered states of disease genes. PDOD utilized drug-drug target interactions with ‘effect type’, an integrated directed molecular network with ‘effect type’ and ‘effect direction’, and disease genes with regulated states in disease patients. With this information, we proposed a scoring function to discover drugs likely to restore altered states of disease genes using the path from a drug to a disease through the drug-drug target interactions, shortest paths from drug targets to disease genes in molecular pathways, and disease gene-disease associations.

Results

We collected drug-drug target interactions, molecular pathways, and disease genes with their regulated states in the diseases. PDOD is applied to 898 drugs with known drug-drug target interactions and nine diseases. We compared performance of PDOD for predicting known therapeutic drug-disease associations with the previous methods. PDOD outperformed other previous approaches which do not exploit directional information in molecular network. In addition, we provide a simple web service that researchers can submit genes of interest with their altered states and will obtain drugs seeming to have opposite effects on altered states of input genes at http://gto.kaist.ac.kr/pdod/index.php/main.

Conclusions

Our results showed that ‘effect type’ and ‘effect direction’ information in the network based approaches can be utilized to identify drugs having opposite effects on diseases. Our study can offer a novel insight into the field of network-based drug repositioning.

     

Seasonal change in xylem growth of <Emphasis Type="Italic">Pinus densiflora</Emphasis> in central Japan

This study investigated the seasonal change in xylem growth of Japanese red pine (Pinus densiflora). Wood cores were sampled at 2-week intervals from April to November in 2012 using the microcoring method. Daily increment rates of tracheid number and tree-ring width were compared with seasonal changes in daily mean temperature and photoperiod. Xylem growth started in early to late May and stopped in late October to early November. The maximum daily increment rates of tracheid number and tree-ring width were in early July. The 95 % confidence intervals of the timing of the maximum daily increment rates included the summer solstice (23 June) with the longest photoperiod, but not the warmest day (30 July). The maximum daily increment rate of xylem growth is thought to be controlled by the photoperiod rather than by temperature. The daily mean temperature exceeded 20 °C after the summer solstice, indicating that temperature is not a limiting factor for xylem growth. This study suggests that the timing of maximum daily increment rates of xylem growth of P. densiflora is controlled by the photoperiod.     

Strategies for Genomic and Proteomic Profiling of Cancers

Omics-based technology platforms have made new kinds of cancer profiling tests feasible. There are several valuable examples in clinical practice, and many more under development. A concerted, transparent process of discovery with lock-down of candidate assays and classifiers and clear specification of intended clinical use is essential. The Institute of Medicine has now proposed a three-stage scheme of confirming and validating analytical findings, validating performance on clinical specimens, and demonstrating explicit clinical utility for an approvable test (Micheel et al., Evolution of translational omics: lessons learned and path forward, 2012).