Experimental and computational studies of strain-conduction velocity relationships in cardiac tissue

Velocity of electrical conduction in cardiac tissue is a function of mechanical strain. Although strain-modulated velocity is a well established finding in experimental cardiology, its underlying mechanisms are not well understood. In this work, we summarized potential factors contributing to strain-velocity relationships and reviewed related experimental and computational studies. We presented results from our experimental studies on rabbit papillary muscle, which supported a biphasic relationship of strain and velocity under uni-axial straining conditions. In the low strain range, the strain-velocity relationship was positive. Conduction velocity peaked with 0.59 m/s at 100% strain corresponding to maximal force development. In the high strain range, the relationship was negative. Conduction was reversibly blocked at 118+/-1.8% strain. Reversible block occurred also in the presence of streptomycin. Furthermore, our studies revealed a moderate hysteresis of conduction velocity, which was reduced by streptomycin. We reconstructed several features of the strain-velocity relationship in a computational study with a myocyte strand. The modeling included strain-modulation of intracellular conductivity and stretch-activated cation non-selective ion channels. The computational study supported our hypotheses, that the positive strain-velocity relationship at low strain is caused by strain-modulation of intracellular conductivity and the negative relationship at high strain results from activity of stretch-activated channels. Conduction block was not reconstructed in our computational studies. We concluded this work by sketching a hypothesis for strain-modulation of conduction and conduction block in papillary muscle. We suggest that this hypothesis can also explain uni-axially measured strain-conduction velocity relationships in other types of cardiac tissue, but apparently necessitates adjustments to reconstruct pressure or volume related changes of velocity in atria and ventricles.     

Substrate tunnels in enzymes: Structure–function relationships and computational methodology

In enzymes, the active site is the location where incoming substrates are chemically converted to products. In some enzymes, this site is deeply buried within the core of the protein, and, in order to access the active site, substrates must pass through the body of the protein via a tunnel. In many systems, these tunnels act as filters and have been found to influence both substrate specificity and catalytic mechanism. Identifying and understanding how these tunnels exert such control has been of growing interest over the past several years because of implications in fields such as protein engineering and drug design. This growing interest has spurred the development of several computational methods to identify and analyze tunnels and how ligands migrate through these tunnels. The goal of this review is to outline how tunnels influence substrate specificity and catalytic efficiency in enzymes with buried active sites and to provide a brief summary of the computational tools used to identify and evaluate these tunnels. Proteins 2015; 83:599–611. © 2015 Wiley Periodicals, Inc.     

Mouse bestrophin-2 is a bona fide Cl(-) channel: identification of a residue important in anion binding and conduction

Bestrophins have recently been proposed to comprise a new family of Cl(-) channels. Our goal was to test whether mouse bestrophin-2 (mBest2) is a bona fide Cl(-) channel. We expressed mBest2 in three different mammalian cell lines. mBest2 was trafficked to the plasma membrane as shown by biotinylation and immunoprecipitation, and induced a Ca(2+)-activated Cl(-) current in all three cell lines (EC(50) for Ca(2+) = 230 nM). The permeability sequence was SCN(-): I(-): Br(-): Cl(-): F(-) (8.2: 1.9: 1.4: 1: 0.5). Although SCN(-) was highly permeant, its conductance was approximately 10% that of Cl(-) and SCN(-) blocked Cl(-) conductance (IC(50) = 12 mM). Therefore, SCN(-) entered the pore more easily than Cl(-), but bound more tightly than Cl(-). Mutations in S79 altered the relative permeability and conductance for SCN(-) as expected if S79 contributed to an anion binding site in the channel. P(SCN)/P(Cl) = 8.2 +/- 1.3 for wild-type and 3.9 +/- 0.4 for S79C. G(SCN)/G(Cl) = 0.14 +/- 0.03 for wild-type and 0.94 +/- 0.04 for S79C. In the S79 mutants, SCN(-) did not block Cl(-) conductance. This suggested that the S79C mutation altered the affinity of an anion binding site for SCN(-). Additional evidence that S79 was located in the conduction pathway was provided by the finding that modification of the sulfhydryl group in S79C with MTSET(+) or MTSES(-) increased conductance significantly. Because the effect of positively and negatively charged MTS reagents was similar, electrostatic interactions between the permeant anion and the channel at this residue were probably not critical in anion selectivity. These data provide strong evidence that mBest2 forms part of the novel Cl(-) conduction pathway in mBest2-transfected cells and that S79 plays an important role in anion binding in the pore of the channel.     

Carbon Quantum Dots–Modified Interfacial Interactions and Ion Conductivity for Enhanced High Current Density Performance in Lithium–Sulfur Batteries

Significant progress has achieved for developing lithium–sulfur (Li–S) batteries with high specific capacities and excellent cyclic stability. However, some critical issues emerge when attempts are made to raise the areal sulfur loading and increase the operation current density to meet the standards for various industrial applications. In this work, polyethylenimine‐functionalized carbon dots (PEI‐CDots) are designed and prepared for enhancing performance of the Li–S batteries with high sulfur loadings and operation under high current density situations. Strong chemical binding effects towards polysulfides and fast ion transport property are achieved in the PEI‐CDots‐modified cathodes. At a high current density of 8 mA cm^?2, the PEI‐CDots‐modified Li–S battery delivers a reversible areal capacity of 3.3 mAh cm^?2 with only 0.07% capacity decay per cycle over 400 cycles at 6.6 mg sulfur loading. Detailed analysis, involving electrochemical impedance spectroscopy, cyclic voltammetry, and density functional theory calculations, is done for the elucidation of the underlying enhancement mechanism by the PEI‐CDots. The strongly localized sulfur species and the promoted Li⁺ ion conductivity at the cathode–electrolyte interface are revealed to enable high‐performance Li–S batteries with high sulfur loading and large operational current.     

Distribution of innervation zone and muscle fiber conduction velocity in the biceps brachii muscle

The spatial distributions of muscle innervation zone (IZ) and muscle fiber conduction velocity (CV) were examined in nine healthy young male participants. High-density surface electromyography (EMG) was collected from the biceps brachii muscle when subjects performed isometric elbow flexions at 20% to 80% of the maximal voluntary contraction (MVC). A total of 9498 samples of IZs were identified and CVs were calculated using the Radon transform. The center and width of IZ sample distribution were compared within four different force levels and six medial to lateral electrode column positions using repeated measures ANOVA and multiple comparison tests. Significant shifts of IZ center were observed in the medial columns (Columns 5, 6, and 7) compared with the lateral columns (Columns 3 and 4) (p < 0.05). Similarly, significant differences in the IZ width were found in Column 7 and 8 compared to Column 3 (p < 0.05). In contrast, muscle CV was unaffected by column position. Instead, muscle CV was faster at 40% and 80% MVC compared to 20% MVC (p < 0.05). The findings of this study add further insights into the physiological properties of the biceps brachii muscle.     

Global pattern and local variation in species–area relationships

Aim We conducted a meta‐analysis of species–area relationships (SARs) by combining several data sets and important covariates such as types of islands, taxonomic groups, latitude and spatial extent, in a hierarchical model framework to study global pattern and local variation in SARs and its consequences for prediction. Location One thousand nine hundred and eighteen islands from 94 SAR studies from around the world. Methods We developed a generalization of the power‐law SAR model, the HSARX model, which allows: (1) the inclusion of multiple focal parameters (intercept, slope, within‐study variance), (2) use of multiple effect modifiers based on a collection of SAR studies, and (3) modelling of the between‐ and within‐study variability. Results The global pattern in the SAR was the average of local SARs and had wide confidence intervals. The global SAR slope was 0.228 with 90% confidence limits of 0.059 and 0.412. The intercept, slope and within‐study variability of local SARs showed great heterogeneity as a result of the interaction of modifying covariates. Confidence intervals for these SAR parameters were narrower when other covariates in addition to area were accounted for, thus increasing the accuracy of the predictions for species richness. The significant effect of latitude and the interaction of latitude, taxa and island type on the SAR slope indicated that the ‘typical’ latitudinal diversity gradient can be reversed in isolated systems. Main conclusions The power‐law relationship underlying the HSARX model provides a good fit for non‐nested SARs across vastly different spatial scales by taking into account other covariates. The HSARX framework allows researchers to explore the complex interactions among SAR parameters and modifying variables, to explicitly study the scale dependence, and to make robust predictions on multiple levels (island, study, global) with associated prediction intervals. From a prediction perspective, it is not the global pattern but the local variation that matters.     

Experimental and computational tools useful for (re)construction of dynamic kinase–substrate networks

The explosion of site‐ and context‐specific in vivo phosphorylation events presents a potentially rich source of biological knowledge and calls for novel data analysis and modeling paradigms. Perhaps the most immediate challenge is delineating detected phosphorylation sites to their effector kinases. This is important for (re)constructing transient kinase–substrate interaction networks that are essential for mechanistic understanding of cellular behaviors and therapeutic intervention, but has largely eluded high‐throughput protein‐interaction studies due to their transient nature and strong dependencies on cellular context. Here, we surveyed some of the computational approaches developed to dissect phosphorylation data detected in systematic proteomic experiments and reviewed some experimental and computational approaches used to map phosphorylation sites to their effector kinases in efforts aimed at reconstructing biological signaling networks.     

Estimating species – area relationships by modeling abundance and frequency subject to incomplete sampling

Models and data used to describe species–area relationships confound sampling with ecological process as they fail to acknowledge that estimates of species richness arise due to sampling. This compromises our ability to make ecological inferences from and about species–area relationships. We develop and illustrate hierarchical community models of abundance and frequency to estimate species richness. The models we propose separate sampling from ecological processes by explicitly accounting for the fact that sampled patches are seldom completely covered by sampling plots and that individuals present in the sampling plots are imperfectly detected. We propose a multispecies abundance model in which community assembly is treated as the summation of an ensemble of species‐level Poisson processes and estimate patch‐level species richness as a derived parameter. We use sampling process models appropriate for specific survey methods. We propose a multispecies frequency model that treats the number of plots in which a species occurs as a binomial process. We illustrate these models using data collected in surveys of early‐successional bird species and plants in young forest plantation patches. Results indicate that only mature forest plant species deviated from the constant density hypothesis, but the null model suggested that the deviations were too small to alter the form of species–area relationships. Nevertheless, results from simulations clearly show that the aggregate pattern of individual species density–area relationships and occurrence probability–area relationships can alter the form of species–area relationships. The plant community model estimated that only half of the species present in the regional species pool were encountered during the survey. The modeling framework we propose explicitly accounts for sampling processes so that ecological processes can be examined free of sampling artefacts. Our modeling approach is extensible and could be applied to a variety of study designs and allows the inclusion of additional environmental covariates.     

Species–area relationships and additive partitioning of diversity of native and nonnative herpetofauna of the West Indies

To evaluate the regional biogeographical patterns of West Indian native and nonnative herpetofauna, we derived and updated data on the presence/absence of all herpetofauna in this region from the recently published reviews. We divided the records into 24 taxonomic groups and classified each species as native or nonnative at each locality. For each taxonomic group and in aggregate, we then assessed the following: (1) multiple species–area relationship (SAR) models; (2) C‐ and Z‐values, typically interpreted to represent insularity or dispersal ability; and (3) the average diversity of islands, among‐island heterogeneity, γ‐diversity, and the contribution of area effect toward explaining among‐island heterogeneity using additive diversity partitioning approach. We found the following: (1) SARs were best modeled using the Cumulative Weibull and Lomolino relationships; (2) the Cumulative Weibull and Lomolino regressions displayed both convex and sigmoid curves; and (3) the Cumulative Weibull regressions were more conservative than Lomolino at displaying sigmoid curves within the range of island size studied. The Z‐value of all herpetofauna was overestimated by Darlington (Zoogeography: The geographic distribution of animals, John Wiley, New York, 1957), and Z‐values were ranked: (1) native > nonnative; (2) reptiles > amphibians; (3) snake > lizard > frog > turtle > crocodilian; and (4) increased from lower‐ to higher‐level taxonomic groups. Additive diversity partitioning showed that area had a weaker effect on explaining the among‐island heterogeneity for nonnative species than for native species. Our findings imply that the flexibility of Cumulative Weibull and Lomolino has been underappreciated in the literature. Z‐value is an average of different slopes from different scales and could be artificially overestimated due to oversampling islands of intermediate to large size. Lower extinction rate, higher colonization, and more in situ speciation could contribute to high richness of native species on large islands, enlarging area effect on explaining the between‐island heterogeneity for native species, whereas economic isolation on large islands could decrease the predicted richness, lowering the area effect for nonnative species. For most of the small islands less affected by human activities, extinction and dispersal limitation are the primary processes producing low species richness pattern, which decreases the overall average diversity with a large among‐island heterogeneity corresponding to the high value of this region as a biodiversity hotspot.     

Latest developments in experimental and computational approaches to characterize protein–lipid interactions

Understanding the functional roles of all the molecules in cells is an ultimate goal of modern biology. An important facet is to understand the functional contributions from intermolecular interactions, both within a class of molecules (e.g. protein–protein) or between classes (e.g. protein‐DNA). While the technologies for analyzing protein–protein and protein–DNA interactions are well established, the field of protein–lipid interactions is still relatively nascent. Here, we review the current status of the experimental and computational approaches for detecting and analyzing protein–lipid interactions. Experimental technologies fall into two principal categories, namely solution‐based and array‐based methods. Computational methods include large–scale data‐driven analyses and predictions/dynamic simulations based on prior knowledge of experimentally identified interactions. Advances in the experimental technologies have led to improved computational analyses and vice versa, thereby furthering our understanding of protein–lipid interactions and their importance in biological systems.     

Differential effects of unaggregated and aggregated amyloid β protein (1–40) on K+ channel currents in primary cultures of rat cerebellar granule and cortical neurones

The effects of amyloid beta protein on voltage-gated K(+) channel currents were studied using the whole-cell patch-clamp technique. The 1-40 amino acid form of amyloid beta protein was applied to primary cultures of rat cerebellar granule and cortical neurones for 24 h. Both the unaggregated and aggregated forms of the peptide, which have differing biological activities, were used. In cerebellar granule neurones, 24-h pre-incubation with 1 microM unaggregated amyloid beta protein resulted in a 60% increase in the 'A'-type component of K(+) current. Increased delayed rectifier activity was Cd(2+)-sensitive and was presumed to be secondary to an increase in voltage-gated Ca(2+) channel current activity. Unaggregated amyloid beta protein had no effect on any component of the K(+) channel current in cortical neurones. One micromolar of aggregated amyloid beta protein had no effect on K(+) channel current in either cell type but reduced cell survival within 24 h as measured using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and terminal deoxynucleotidyl transferase-mediated dUTP nick end labelling (TUNEL) assays. The unaggregated form of amyloid beta protein had no neurotoxic effects when applied to either neurone type for up to 72 h. These data indicate that the unaggregated, non-pathological form of amyloid beta protein causes changes in the ion channel function of neurones, possibly reflecting a physiological role for the peptide.     

Human–Animal Bonds Between Zoo Professionals and the Animals in Their Care

Some human‐animal relationships can be so positive that they confer emotional well‐being to both partners and can thus be viewed as bonds. In this study, 130 delegates at zoo research and training events completed questionnaires in which they were asked about their professional work in the zoo and whether they believed they had established bonds with any animals. They were also asked to indicate agreement or disagreement with several statements about human‐animal bonds. Results showed that many zoo professionals consider that they have established bonds with some of their animals; 103 respondents believed that they had a bond with at least one animal, and 78 of these identified that the bond was with a zoo animal. The most frequent bonds reported were with primates (n = 24) and carnivores (n = 28). Perceived benefits of these bonds to the respondents included both operational (animal easier to handle, easier to administer treatments to) and affective (sense of well‐being, enjoyment at being with the animal). Identifying benefits to the animals was more difficult. Most respondents identified similar benefits for their animals as for themselves, i.e. operational (animal responded more calmly, appeared less stressed) and affective (animal appeared to enjoy contact with respondent, seemed more content). This suggests that bonding between zoo professionals and their animals could have profound consequences for the management and welfare of the animals, not to mention the job satisfaction of the people involved. Zoo Biol 31:13;–26, 2012. © 2010 Wiley Periodicals, Inc.     

Simulation of ATP metabolism in cardiac excitation–contraction coupling

To obtain insights into the mechanisms underlying the membrane excitation and contraction of cardiac myocytes, we developed a computer model of excitation–contraction coupling (Kyoto model: Jpn. J. Physiol. 53 (2003) 105). This model was further expanded by incorporating pivotal reactions of ATP metabolism; the model of mitochondrial oxidative phosphorylation by Korzeniewski and Zoladz (Biophys. Chem. 92 (2001) 17). The ATP-dependence of contraction, and creatine kinase and adenylate kinase were also incorporated. After minor modifications, the steady-state condition was well established for all the variables, including the membrane potential, contraction, and the ion and metabolite concentrations in sarcoplasmic reticulum, mitochondria and cytoplasm. Concentrations of major metabolites were close to the experimental data. Responses of the new model to anoxia were similar to experimental results of the P-31 NMR study in whole heart. This model serves as a prototype for developing a more comprehensive model of excitation–contraction–metabolism coupling.