Search

Increasing evidence has indicated that long non-coding RNAs (lncRNAs) are implicated in and associated with many complex human diseases. Despite of the accumulation of lncRNA-disease associations, only a few studies had studied the roles of these associations in pathogenesis. In this paper, we investigated lncRNA-disease associations from a network view to understand the contribution of these lncRNAs to complex diseases. Specifically, we studied both the properties of the diseases in which the lncRNAs were implicated, and that of the lncRNAs associated with complex diseases. Regarding the fact that protein coding genes and lncRNAs are involved in human diseases, we constructed a coding-non-coding gene-disease bipartite network based on known associations between diseases and disease-causing genes. We then applied a propagation algorithm to uncover the hidden lncRNA-disease associations in this network. The algorithm was evaluated by leave-one-out cross validation on 103 diseases in which at least two genes were known to be involved, and achieved an AUC of 0.7881. Our algorithm successfully predicted 768 potential lncRNA-disease associations between 66 lncRNAs and 193 diseases. Furthermore, our results for Alzheimer's disease, pancreatic cancer, and gastric cancer were verified by other independent studies. [ABSTRACT FROM AUTHOR]

The regulation of chromosome separation during mitosis is not fully understood yet. Microtubules forming mitotic spindles are targets of treatment strategies which are aimed at (i) the triggering of the apoptosis or (ii) the interruption of uncontrolled cell division. Despite these facts, only few physical models relating to the dynamics of mitotic spindles exist up to now. In this paper, we present the first electromechanical model which enables calculation of the electromagnetic field coupled to acoustic vibrations of the mitotic spindle. This electromagnetic field originates from the electrical polarity of microtubules which form the mitotic spindle. The model is based on the approximation of resonantly vibrating microtubules by a network of oscillating electric dipoles. Our computational results predict the existence of a rapidly changing electric field which is generated by either driven or endogenous vibrations of the mitotic spindle. For certain values of parameters, the intensity of the electric field and its gradient reach values which may exert a not-inconsiderable force on chromosomes which are aligned in the spindle midzone. Our model may describe possible mechanisms of the effects of ultra-short electrical and mechanical pulses on dividing cells—a strategy used in novel methods for cancer treatment. [ABSTRACT FROM AUTHOR]

Cortical spreading depression (CSD) is a pathophysiological phenomenon, which underlies some neurological disorders, such as migraine and stroke, but its mechanisms are still not completely understood. One of the striking facts is that the spatiotemporal evolution of CSD wave is varying. Observations in experiments reveal that a CSD wave may propagate through the entire cortex, or just bypass some areas of the cortex. In this paper, we have applied a 2D reaction-diffusion equation with recovery term to study the spatiotemporal evolution of CSD. By modulating the recovery rate from CSD in the modeled cortex, CSD waves with different spatiotemporal evolutions, either bypassing some areas or propagating slowly in these areas, were present. Moreover, spiral CSD waves could also be induced in case of the transiently altered recovery rate, i.e. block release from the absolute refractory period. These results suggest that the refractory period contributes to the different propagation patterns of CSD, which may help to interpret the mechanisms of CSD propagation. [ABSTRACT FROM AUTHOR]

Sudden cardiac death is often caused by cardiac arrhythmias. Recently, special attention has been given to a certain arrhythmogenic condition, the long-QT syndrome, which occurs as a result of genetic mutations or drug toxicity. The underlying mechanisms of arrhythmias, caused by the long-QT syndrome, are not fully understood. However, arrhythmias are often connected to special excitations of cardiac cells, called early afterdepolarizations (EADs), which are depolarizations during the repolarizing phase of the action potential. So far, EADs have been studied mainly in isolated cardiac cells. However, the question on how EADs at the single-cell level can result in fibrillation at the tissue level, especially in human cell models, has not been widely studied yet. In this paper, we study wave patterns that result from single-cell EAD dynamics in a mathematical model for human ventricular cardiac tissue. We induce EADs by modeling experimental conditions which have been shown to evoke EADs at a single-cell level: by an increase of L-type Ca currents and a decrease of the delayed rectifier potassium currents. We show that, at the tissue level and depending on these parameters, three types of abnormal wave patterns emerge. We classify them into two types of spiral fibrillation and one type of oscillatory dynamics. Moreover, we find that the emergent wave patterns can be driven by calcium or sodium currents and we find phase waves in the oscillatory excitation regime. From our simulations we predict that arrhythmias caused by EADs can occur during normal wave propagation and do not require tissue heterogeneities. Experimental verification of our results is possible for experiments at the cell-culture level, where EADs can be induced by an increase of the L-type calcium conductance and by the application of I blockers, and the properties of the emergent patterns can be studied by optical mapping of the voltage and calcium. [ABSTRACT FROM AUTHOR]

Knowledge of how a population of cancerous cells progress through the cell cycle is vital if the population is to be treated effectively, as treatment outcome is dependent on the phase distributions of the population. Estimates on the phase distribution may be obtained experimentally however the errors present in these estimates may effect treatment efficacy and planning. If mathematical models are to be used to make accurate, quantitative predictions concerning treatments, whose efficacy is phase dependent, knowledge of the phase distribution is crucial. In this paper it is shown that two different transition rates at the - checkpoint provide a good fit to a growth curve obtained experimentally. However, the different transition functions predict a different phase distribution for the population, but both lying within the bounds of experimental error. Since treatment outcome is effected by the phase distribution of the population this difference may be critical in treatment planning. Using an age-structured population balance approach the cell cycle is modelled with particular emphasis on the - checkpoint. By considering the probability of cells transitioning at the - checkpoint, different transition functions are obtained. A suitable finite difference scheme for the numerical simulation of the model is derived and shown to be stable. The model is then fitted using the different probability transition functions to experimental data and the effects of the different probability transition functions on the model's results are discussed. [ABSTRACT FROM AUTHOR]

Thromboembolic disease is a leading cause of morbidity and mortality worldwide. In the last several years there have been a number of studies attempting to identify mechanisms that stop thrombus growth. This paper identifies a novel mechanism related to formation of a fibrin cap. In particular, protein transport through a fibrin network, an important component of a thrombus, was studied by integrating experiments with model simulations. The network permeability and the protein diffusivity were shown to be important factors determining the transport of proteins through the fibrin network. Our previous in vivo studies in mice have shown that stabilized non-occluding thrombi are covered by a fibrin network (‘fibrin cap’). Model simulations, calibrated using experiments in microfluidic devices and accounting for the permeable structure of the fibrin cap, demonstrated that thrombin generated inside the thrombus was washed downstream through the fibrin network, thus limiting exposure of platelets on the thrombus surface to thrombin. Moreover, by restricting the approach of resting platelets in the flowing blood to the thrombus core, the fibrin cap impaired platelets from reaching regions of high thrombin concentration necessary for platelet activation and limited thrombus growth. The formation of a fibrin cap prevents small thrombi that frequently develop in the absence of major injury in the 60000 km of vessels in the body from developing into life threatening events. [ABSTRACT FROM AUTHOR]

Computational prediction of interactions between drugs and their target proteins is of great importance for drug discovery and design. The difficulties of developing computational methods for the prediction of such potential interactions lie in the rarity of known drug-protein interactions and no experimentally verified negative drug-target interaction sample. Furthermore, target proteins need also to be predicted for some new drugs without any known target interaction information. In this paper, a semi-supervised learning method NetCBP is presented to address this problem by using labeled and unlabeled interaction information. Assuming coherent interactions between the drugs ranked by their relevance to a query drug, and the target proteins ranked by their relevance to the hidden target proteins of the query drug, we formulate a learning framework maximizing the rank coherence with respect to the known drug-target interactions. When applied to four classes of important drug-target interaction networks, our method improves previous methods in terms of cross-validation and some strongly predicted interactions are confirmed by the publicly accessible drug target databases, which indicates the usefulness of our method. Finally, a comprehensive prediction of drug–target interactions enables us to suggest many new potential drug–target interactions for further studies. [ABSTRACT FROM AUTHOR]

A variety of cardiac arrhythmias are initiated by a focal excitation that disrupts the regular beating of the heart. In some cases it is known that these excitations are due to calcium (Ca) release from the sarcoplasmic reticulum (SR) via propagating subcellular Ca waves. However, it is not understood what are the physiological factors that determine the timing of these excitations at both the subcellular and tissue level. In this paper we apply analytic and numerical approaches to determine the timing statistics of spontaneous Ca release (SCR) in a simplified model of a cardiac myocyte. In particular, we compute the mean first passage time (MFPT) to SCR, in the case where SCR is initiated by spontaneous Ca sparks, and demonstrate that this quantity exhibits either an algebraic or exponential dependence on system parameters. Based on this analysis we identify the necessary requirements so that SCR occurs on a time scale comparable to the cardiac cycle. Finally, we study how SCR is synchronized across many cells in cardiac tissue, and identify a quantitative measure that determines the relative timing of SCR in an ensemble of cells. Using this approach we identify the physiological conditions so that cell-to-cell variations in the timing of SCR is small compared to the typical duration of an SCR event. We argue further that under these conditions inward currents due to SCR can summate and generate arrhythmogenic triggered excitations in cardiac tissue. [ABSTRACT FROM AUTHOR]

Calpain is an intracellular Ca2+ -activated protease that is involved in numerous Ca2+ dependent regulation of protein function in many cell types. This paper tests a hypothesis that calpains are involved in Ca2+ -dependent increase of the late sodium current (INaL) in failing heart. Chronic heart failure (HF) was induced in 2 dogs by multiple coronary artery embolization. Using a conventional patch-clamp technique, the whole-cell INaL was recorded in enzymatically isolated ventricular cardiomyocytes (VCMs) in which INaL was activated by the presence of a higher (1μM) intracellular [Ca2+] in the patch pipette. Cell suspensions were exposed to a cell- permeant calpain inhibitor MDL-28170 for 1–2 h before INaL recordings. The numerical excitation-contraction coupling (ECC) model was used to evaluate electrophysiological effects of calpain inhibition in silico. MDL caused acceleration of INaL decay evaluated by the two-exponential fit (τ1 = 42±3.0 ms τ2 = 435±27 ms, n = 6, in MDL vs. τ1 = 52±2.1 ms τ2 = 605±26 control no vehicle, n = 11, and vs. τ1 = 52±2.8 ms τ2 = 583±37 ms n = 7, control with vehicle, P<0.05 ANOVA). MDL significantly reduced INaL density recorded at –30 mV (0.488±0.03, n = 12, in control no vehicle, 0.4502±0.0210, n = 9 in vehicle vs. 0.166±0.05pA/pF, n = 5, in MDL). Our measurements of current-voltage relationships demonstrated that the INaL density was decreased by MDL in a wide range of potentials, including that for the action potential plateau. At the same time the membrane potential dependency of the steady-state activation and inactivation remained unchanged in the MDL-treated VCMs. Our ECC model predicted that calpain inhibition greatly improves myocyte function by reducing the action potential duration and intracellular diastolic Ca2+ accumulation in the pulse train. Conclusions: Calpain inhibition reverses INaL changes in failing dog ventricular cardiomyocytes in the presence of high intracellular Ca2+. Specifically it decreases INaL density and accelerates INaL kinetics resulting in improvement of myocyte electrical response and Ca2+ handling as predicted by our in silico simulations. [ABSTRACT FROM AUTHOR]