The effects of aerobic exercise training on the age-related lipid peroxidation, Schwann cell apoptosis and ultrastructural changes in the sciatic nerve of rats

The potential role of exercise in preventing the age-related spontaneous peripheral neuropathy has not been studied. We examined the effects of long-term aerobic exercise training on lipid peroxidation, Schwann cell (SC) apoptosis and ultrastructural changes in the sciatic nerve of rats during aging. Three groups of 12-week old Wistar rats ran on a treadmill for 6, 9 and 12 months (exercise trained (ET) group, n=10 each) according to an exercise training program targeted at a speed of 22 m/min (at 7 degrees incline), 60 min/day, 6 days/week. Three corresponding groups of untrained rats were used as the controls (sedentary (SED) group). At the end of each period, sciatic nerve biopsies were performed, and processed for biochemical, immunohistochemical and ultrastructural analyses. The results showed that aging was associated with an increased level of nerve malondialdehyde (MDA, marker of lipid peroxidation) and a higher number of SC apoptosis in SED group. The SED group showed irregular nerve fibers with thin myelin sheaths and areas of myelin-axon detachment. However, the ET group had significantly diminished nerve lipid peroxidation and SC apoptosis. In the ET group, nerve fibers had a thick myelin sheath with frequent folding. These findings suggest that aerobic exercise training protects peripheral nerves by attenuating oxidative reactions, and preserving SCs and myelin sheath from pathologic changes, which occur during normal aging.     

The effect of water stress on the activities of key regulatory enzymes of the sucrose to starch pathway in wheat

Developmental changes in the starch and sucrose content of grains andthe activities of enzymes of starch synthesis in wheat were studied under waterstress conditions. Water stress caused a marked reduction in the sucrose andstarch content of the grains. Sucrose synthase (SS) and UDP-glucosepyrophosphorylase (UDP-Gppase), showed higher catalytic activity and moreresistance to water stress compared with amyloplastic enzymes. ADP-glucosepyrophosphorylase (ADP-Gppase) activity was reduced to a low level under bothin situ and osmotic stress conditions in which grainsfailed to accumulate dry matter in vivo. Granule-boundstarch synthase (GBSS) also responded rapidly to in situwater stress treatments as did ADP-Gppase. Reduction in GBSS activity at thetime of growth cessation in situ was less than that ofADP-Gppase and the enzyme did not respond to severe osmotic stress. Solublestarch synthase (SSS) was the enzyme most sensitive to water stress in that itresponded earlier, and to a greater extent, than the other enzymes. However,under severe dehydration conditions, leading to cessation of growth, thedeclinein SSS activity was less than that for ADP-Gppase. SSS showed the lowestin vitro activity followed by GBSS. These results suggestthat SSS is the site of response to water stress by which the rate of graingrowth can be affected, whereas growth cessation is due mainly to theinactivation of ADP-Gppase.     

Extracellular‐signal‐regulated kinase/mitogen‐activated protein kinase signaling as a target for cancer therapy: an updated review

Mitogen‐activated protein kinase (MAPK) signaling pathway is activated in a wide spectrum of human tumors, exhibiting cardinal oncogenic roles and sustained inhibition of this pathway is considered as a primary goal in clinic. Within this pathway, receptor tyrosine kinases such as epithelial growth factor receptor, mesenchymal–epithelial transition, and AXL act as upstream regulators of RAS/RAF/MEK/extracellular‐signal‐regulated kinase. MAPK signaling is active in both early and advanced stages of tumorigenesis, and it promotes tumor proliferation, survival, and metastasis. MAPK regulatory effects on cellular constituent of the tumor microenvironment is for immunosuppressive purposes. Cross‐talking between MAPK with oncogenic signaling pathways including WNT, cyclooxygenase‐2, transforming growth factor‐β, NOTCH and (in particular) with phosphatidylinositol 3‐kinase is contributed to the multiplication of tumor progression and drug resistance. Developing resistance (intrinsic or acquired) to MAPK‐targeted therapy also occurs due to heterogeneity of tumors along with mutations and negative feedback loop of interactions exist between various kinases causing rebound activation of this signaling. Multidrug regimen is a preferred therapeutic avenue for targeting MAPK signaling. To enhance patient tolerance and to mitigate potential adversarial effects related to the combination therapy, determination of a desired dose and drug along with pre‐evaluation of cancer‐type‐specific kinase mutation and sensitivity, especially for patients receiving triplet therapy is an urgent need.     

AKT1 and SELP Polymorphisms Predict the Risk of Developing Cachexia in Pancreatic Cancer Patients

Pancreatic ductal adenocarcinoma (PDAC) patients have the highest risk of developing cachexia, which is a direct cause of reduced quality of life and shorter survival. Novel biomarkers to identify patients at risk of cachexia are needed and might have a substantial impact on clinical management. Here we investigated the prognostic value and association of SELP-rs6136, IL6-rs1800796 and AKT1-rs1130233 polymorphisms with cachexia in PDAC. Genotyping was performed in DNA from blood samples of a test and validation cohorts of 151 and 152 chemo-naive locally-advanced/metastatic PDAC patients, respectively. The association of SELP-rs6136, IL6-rs1800796 and AKT1-rs1130233 polymorphisms with cachexia as well as the correlation between cachexia and the candidate polymorphisms and overall survival were analyzed. Akt expression and phosphorylation in muscle biopsies were evaluated by specific ELISA assays. SELP-rs6136-AA and AKT1-rs1130233-AA/GA genotypes were associated with increased risk of developing cachexia in both cohorts (SELP: p = 0.011 and p = 0.045; AKT1: p = 0.004 and p = 0.019 for the first and second cohorts, respectively), while patients carrying AKT1-rs1130233-GG survived significantly longer (p = 0.002 and p = 0.004 for the first and second cohorts, respectively). In the multivariate analysis AKT1-rs1130233-AA/GA genotypes were significant predictors for shorter survival, with an increased risk of death of 1.7 (p = 0.002) and 1.6 (p = 0.004), in the first and second cohorts, respectively. This might be explained by the reduced phosphorylation of Akt1 in muscle biopsies from patients harboring AKT1-rs1130233-AA/GA (p = 0.003), favoring apoptosis induction. In conclusion, SELP and AKT1 polymorphisms may play a role in the risk of cachexia and death in PDAC patients, and should be further evaluated in larger prospective studies.     

Accurate prediction of enzyme mutant activity based on a multibody statistical potential

MOTIVATION: An important area of research in biochemistry and molecular biology focuses on characterization of enzyme mutants. However, synthesis and analysis of experimental mutants is time consuming and expensive. We describe a machine-learning approach for inferring the activity levels of all unexplored single point mutants of an enzyme, based on a training set of such mutants with experimentally measured activity. RESULTS: Based on a Delaunay tessellation-derived four-body statistical potential function, a perturbation vector measuring environmental changes relative to wild type (wt) at every residue position uniquely characterizes each enzyme mutant for model development and prediction. First, a measure of model performance utilizing area (AUC) under the receiver operating characteristic (ROC) curve surpasses 0.83 and 0.77 for data sets of experimental HIV-1 protease and T4 lysozyme mutants, respectively. Additionally, a novel method is introduced for evaluating statistical significance associated with the number of correct test set predictions obtained from a trained model. Third, 100 stratified random splits of the protease and T4 lysozyme mutant data sets into training and test sets achieve 77.0% and 80.8% mean accuracy, respectively. Next, protease and T4 lysozyme models trained with experimental mutants are used to predict activity levels for all remaining mutants; a subsequent search for publications reporting on dozens of these test mutants reveals that experimental results are matched by 79% and 86% of predictions, respectively. Finally, learning curves for each mutant enzyme system indicate the influence of training set size on model performance. AVAILABILITY: Prediction databases at http://proteins.gmu.edu/automute/     

Predicting helium and neon adsorption and separation on carbon nanotubes by Monte Carlo simulation

The adsorption of helium and neon mixtures on single-walled carbon nanotubes (SWCNTs) was investigated at various temperatures (subcritical and supercritical) and pressures using canonical Monte Carlo (CMC) simulation. Adsorption isotherms were obtained at different temperatures (4, 40, 77 and 130 K) and pressures ranging from 1 to 16 MPa. Separation factors and isosteric enthalpies of adsorption were also calculated. Moreover, the adsorption isotherms were obtained at constant specific temperatures (4 and 40 K) and pressures (0.2 and 1.0 MPa) as a function of the amount adsorbed. All of the adsorption isotherms for an equimolar mixture of helium and neon have a Langmuir shape, indicating that no capillary condensation occurs. Both the helium and the neon adsorption isotherms exhibit similar behavior, and slightly more of the helium and neon mixture is adsorbed on the inner surfaces of the SWCNTs than on their outer surfaces. More neon is adsorbed than helium within the specified pressure range. The data obtained show that the isosteric enthalpies for the adsorption of neon are higher than those for helium under the same conditions, which means that adsorption of neon preferentially occurs by (15, 15) SWCNTs. Furthermore, the isosteric enthalpies of adsorption of both gases decrease with increasing temperature.     

Four-body atomic potential for modeling protein-ligand binding affinity: application to enzyme-inhibitor binding energy prediction

Background

Models that are capable of reliably predicting binding affinities for protein-ligand complexes play an important role the field of structure-guided drug design.

Methods

Here, we begin by applying the computational geometry technique of Delaunay tessellation to each set of atomic coordinates for over 1400 diverse macromolecular structures, for the purpose of deriving a four-body statistical potential that serves as a topological scoring function. Next, we identify a second, independent set of three hundred protein-ligand complexes, having both high-resolution structures and known dissociation constants. Two-thirds of these complexes are randomly selected to train a predictive model of binding affinity as follows: two tessellations are generated in each case, one for the entire complex and another strictly for the isolated protein without its bound ligand, and a topological score is computed for each tessellation with the four-body potential. Predicted protein-ligand binding affinity is then based on an empirically derived linear function of the difference between both topological scores, one that appropriately scales the value of this difference.

Results

A comparison between experimental and calculated binding affinity values over the two hundred complexes reveals a Pearson's correlation coefficient of r = 0.79 with a standard error of SE = 1.98 kcal/mol. To validate the method, we similarly generated two tessellations for each of the remaining protein-ligand complexes, computed their topological scores and the difference between the two scores for each complex, and applied the previously derived linear transformation of this topological score difference to predict binding affinities. For these one hundred complexes, we again observe a correlation of r = 0.79 (SE = 1.93 kcal/mol) between known and calculated binding affinities. Applying our model to an independent test set of high-resolution structures for three hundred diverse enzyme-inhibitor complexes, each with an experimentally known inhibition constant, also yields a correlation of r = 0.79 (SE = 2.39 kcal/mol) between experimental and calculated binding energies.

Conclusions

Lastly, we generate predictions with our model on a diverse test set of one hundred protein-ligand complexes previously used to benchmark 15 related methods, and our correlation of r = 0.66 between the calculated and experimental binding energies for this dataset exceeds those of the other approaches. Compared with these related prediction methods, our approach stands out based on salient features that include the reliability of our model, combined with the rapidity of the generated predictions, which are less than one second for an average sized complex.

     

Hytrosavirus genetic diversity and eco-regional spread in <Emphasis Type="Italic">Glossina</Emphasis> species

Background

The management of the tsetse species Glossina pallidipes (Diptera; Glossinidae) in Africa by the sterile insect technique (SIT) has been hindered by infections of G. pallidipes production colonies with Glossina pallidipes salivary gland hypertrophy virus (GpSGHV; Hytrosaviridae family). This virus can significantly decrease productivity of the G. pallidipes colonies. Here, we used three highly diverged genes and two variable number tandem repeat regions (VNTRs) of the GpSGHV genome to identify the viral haplotypes in seven Glossina species obtained from 29 African locations and determine their phylogenetic relatedness.

Results

GpSGHV was detected in all analysed Glossina species using PCR. The highest GpSGHV prevalence was found in G. pallidipes colonized at FAO/IAEA Insect Pest Control Laboratory (IPCL) that originated from Uganda (100%) and Tanzania (88%), and a lower prevalence in G. morsitans morsitans from Tanzania (58%) and Zimbabwe (20%). Whereas GpSGHV was detected in 25–40% of G. fuscipes fuscipes in eastern Uganda, the virus was not detected in specimens of neighboring western Kenya. Most of the identified 15 haplotypes were restricted to specific Glossina species in distinct locations. Seven haplotypes were found exclusively in G. pallidipes. The reference haplotype H1 (GpSGHV-Uga; Ugandan strain) was the most widely distributed, but was not found in G. swynnertoni GpSGHV. The 15 haplotypes clustered into three distinct phylogenetic clades, the largest contained seven haplotypes, which were detected in six Glossina species. The G. pallidipes-infecting haplotypes H10, H11 and H12 (from Kenya) clustered with H7 (from Ethiopia), which presumably corresponds to the recently sequenced GpSGHV-Eth (Ethiopian) strain. These four haplotypes diverged the most from the reference H1 (GpSGHV-Uga). Haplotypes H1, H5 and H14 formed three main genealogy hubs, potentially representing the ancestors of the 15 haplotypes.

Conclusion

These data identify G. pallidipes as a significant driver for the generation and diversity of GpSGHV variants. This information may provide control guidance when new tsetse colonies are established and hence, for improved management of the virus in tsetse rearing facilities that maintain multiple Glossina species.

     

Genetic variants in the WNT signaling pathway are protectively associated with colorectal cancer in a Saudi population

The Wnt/β-catenin signaling pathway has been etiologically implicated in the development and progression of colorectal cancer. We studied thirteen single nucleotide polymorphisms (SNPs) located in SFRP3 (rs7775), CTNNB1 (β-catenin) [rs4135385, rs13072632], APC (rs454886, rs459552), LRP6 (rs2075241, rs2284396), DKK4 (rs3763511), DKK3 (rs6485350), TCF4 (rs12255372) and AXIN2 (rs3923086, rs3923087, rs4791171) in patients with colorectal cancer (n?=?122) and controls (n?=?110). Evaluation of WNT pathway SNPs showed protective association for rs4135385, located in β-catenin. Additionally, variants in SFRP3 (rs7775) and LRP6 (rs2284396) which did not show any association in the overall analysis were significantly associated with female and old aged colorectal cancer patients, respectively.