Isoenzyme Changes during Seed Germination of Saguaro Cactus (Carnegiea gigantea)

Esterases, acid phosphatases, leucine aminopeptidases, peroxidases and glutamic-oxalacetic transaminases from extracts made from seeds of Saguaro cactus at different stages of germination were studied using starch gelelectrophoretic techniques. In general, four of the five enzyme systems showed significant increase in the number of isoenzymes and their activity after 72 hours of germination. It is suggested that Saguaro cactus seeds reach their enzymatically most active state between 48 to 72 hours after being placed at optimum moisture conditions at 30°C in continuous light.     

Identification of a redox-sensitive switch within the JAK2 catalytic domain

Four cysteine residues (Cys866, Cys917, Cys1094, and Cys1105) have direct roles in cooperatively regulating Janus kinase 2 (JAK2) catalytic activity. Additional site-directed mutagenesis experiments now provide evidence that two of these residues (Cys866 and Cys917) act together as a redox-sensitive switch, allowing JAK2's catalytic activity to be directly regulated by the redox state of the cell. We created several variants of the truncated JAK2 (GST/(NΔ661)rJAK2), which incorporated cysteine-to-serine or cysteine-to-alanine mutations. The catalytic activities of these mutant enzymes were evaluated by in vitro autokinase assays and by in situ autophosphorylation and transphosphorylation assays. Cysteine-to-alanine mutagenesis revealed that the mechanistic role of Cys866 and Cys917 is functionally distinct from that of Cys1094 and Cys1105. Most notable is the observation that the robust activity of the CC866,917AA mutant is unaltered by pretreatment with dithiothreitol or o-iodosobenzoate, unlike all other JAK2 variants previously examined. This work provides the first direct evidence for a cysteine-based redox-sensitive switch that regulates JAK2 catalytic activity. The presence of this redox-sensitive switch predicts that reactive oxygen species can impair the cell's response to JAK-coupled cytokines under conditions of oxidative stress, which we confirm in a murine pancreatic β-islet cell line.     

Does Video Gaming Affect Orthopaedic Skills Acquisition? A Prospective Cohort-Study

Introduction

Previous studies have suggested that there is a positive correlation between the extent of video gaming and efficiency of surgical skill acquisition on laparoscopic and endovascular surgical simulators amongst trainees. However, the link between video gaming and orthopaedic trauma simulation remains unexamined, in particular dynamic hip screw (DHS) stimulation.

Objective

To assess effect of prior video gaming experience on virtual-reality (VR) haptic-enabled DHS simulator performance.

Methods

38 medical students, naïve to VR surgical simulation, were recruited and stratified relative to their video gaming exposure. Group 1 (n = 19, video-gamers) were defined as those who play more than one hour per day in the last calendar year. Group 2 (n = 19, non-gamers) were defined as those who play video games less than one hour per calendar year. Both cohorts performed five attempts on completing a VR DHS procedure and repeated the task after a week. Metrics assessed included time taken for task, simulated flouroscopy time and screw position. Median and Bonett-Price 95% confidence intervals were calculated for seven real-time objective performance metrics. Data was confirmed as non-parametric by the Kolmogorov-Smirnov test. Analysis was performed using the Mann-Whitney U test for independent data whilst the Wilcoxon signed ranked test was used for paired data. A result was deemed significant when a two-tailed p-value was less than 0.05.

Results

All 38 subjects completed the study. The groups were not significantly different at baseline. After ten attempts, there was no difference between Group 1 and Group 2 in any of the metrics tested. These included time taken for task, simulated fluoroscopy time, number of retries, tip-apex distance, percentage cut-out and global score.

Conclusion

Contrary to previous literature findings, there was no correlation between video gaming experience and gaining competency on a VR DHS simulator.     

Parameterization and classification of the protein universe via geometric techniques

We present a scheme for the classification of 3487 non-redundant protein structures into 1207 non-hierarchical clusters by using recurring structural patterns of three to six amino acids as keys of classification. This results in several signature patterns, which seem to decide membership of a protein in a functional category. The patterns provide clues to the key residues involved in functional sites as well as in protein-protein interaction. The discovered patterns include a "glutamate double bridge" of superoxide dismutase, the functional interface of the serine protease and inhibitor, interface of homo/hetero dimers, and functional sites of several enzyme families. We use geometric invariants to decide superimposability of structural patterns. This allows the parameterization of patterns and discovery of recurring patterns via clustering. The geometric invariant-based approach eliminates the computationally explosive step of pair-wise comparison of structures. The results provide a vast resource for the biologists for experimental validation of the proposed functional sites, and for the design of synthetic enzymes, inhibitors and drugs.     

A stochastic analysis of first-order reaction networks

A stochastic model for a general system of first-order reactions in which each reaction may be either a conversion reaction or a catalytic reaction is derived. The governing master equation is formulated in a manner that explicitly separates the effects of network topology from other aspects, and the evolution equations for the first two moments are derived. We find the surprising, and apparently unknown, result that the time evolution of the second moments can be represented explicitly in terms of the eigenvalues and projections of the matrix that governs the evolution of the means. The model is used to analyze the effects of network topology and the reaction type on the moments of the probability distribution. In particular, it is shown that for an open system of first-order conversion reactions, the distribution of all the system components is a Poisson distribution at steady state. Two different measures of the noise have been used previously, and it is shown that different qualitative and quantitative conclusions can result, depending on which measure is used. The effect of catalytic reactions on the variance of the system components is also analyzed, and the master equation for a coupled system of first-order reactions and diffusion is derived. All authors contributed equally to this work.     

Membrane Permeation Induced by Aggregates of Human Islet Amyloid Polypeptides

Several neurodegenerative diseases such as Alzheimer’s and Parkinson’s diseases as well as nonneuropathic diseases such as type II diabetes and atrial amyloidosis are associated with aggregation of amyloid polypeptides into fibrillar structures, or plaques. In this study, we use molecular dynamics simulations to test the stability and orientation of membrane-embedded aggregates of the human islet amyloid polypeptide (hIAPP) implicated in type II diabetes. We find that in both monolayers and bilayers of dipalmitoylphosphatidylglycerol (DPPG) hIAPP trimers and tetramers remain inside the membranes and preserve their β-sheet secondary structure. Lipid bilayer-inserted hIAPP trimers and tetramers orient inside DPPG at 60° relative to the membrane/water interface and lead to water permeation and Na⁺ intrusion, consistent with ion-toxicity in islet β-cells. In particular, hIAPP trimers form a water-filled β-sandwich that induce water permeability comparable with channel-forming proteins, such as aquaporins and gramicidin-A. The predicted disruptive orientation is consistent with the amphiphilic properties of the hIAPP aggregates and could be probed by chiral sum frequency generation (SFG) spectroscopy, as predicted by the simulated SFG spectra.     

A top-down approach to classify enzyme functional classes and sub-classes using random forest

Advancements in sequencing technologies have witnessed an exponential rise in the number of newly found enzymes. Enzymes are proteins that catalyze bio-chemical reactions and play an important role in metabolic pathways. Commonly, function of such enzymes is determined by experiments that can be time consuming and costly. Hence, a need for a computing method is felt that can distinguish protein enzyme sequences from those of non-enzymes and reliably predict the function of the former. To address this problem, approaches that cluster enzymes based on their sequence and structural similarity have been presented. But, these approaches are known to fail for proteins that perform the same function and are dissimilar in their sequence and structure. In this article, we present a supervised machine learning model to predict the function class and sub-class of enzymes based on a set of 73 sequence-derived features. The functional classes are as defined by International Union of Biochemistry and Molecular Biology. Using an efficient data mining algorithm called random forest, we construct a top-down three layer model where the top layer classifies a query protein sequence as an enzyme or non-enzyme, the second layer predicts the main function class and bottom layer further predicts the sub-function class. The model reported overall classification accuracy of 94.87% for the first level, 87.7% for the second, and 84.25% for the bottom level. Our results compare very well with existing methods, and in many cases report better performance. Using feature selection methods, we have shown the biological relevance of a few of the top rank attributes.     

Indirect inhibition of 26S proteasome activity in a cellular model of Huntington's disease

Pathognomonic accumulation of ubiquitin (Ub) conjugates in human neurodegenerative diseases, such as Huntington's disease, suggests that highly aggregated proteins interfere with 26S proteasome activity. In this paper, we examine possible mechanisms by which an N-terminal fragment of mutant huntingtin (htt; N-htt) inhibits 26S function. We show that ubiquitinated N-htt-whether aggregated or not-did not choke or clog the proteasome. Both Ub-dependent and Ub-independent proteasome reporters accumulated when the concentration of mutant N-htt exceeded a solubility threshold, indicating that stabilization of 26S substrates is not linked to impaired Ub conjugation. Above this solubility threshold, mutant N-htt was rapidly recruited to cytoplasmic inclusions that were initially devoid of Ub. Although synthetically polyubiquitinated N-htt competed with other Ub conjugates for access to the proteasome, the vast majority of mutant N-htt in cells was not Ub conjugated. Our data confirm that proteasomes are not directly impaired by aggregated N-terminal fragments of htt; instead, our data suggest that Ub accumulation is linked to impaired function of the cellular proteostasis network.