Identification of oxytocin-related lncRNAs and assessment of their expression in breast cancer.

Oxytocin is a neuropeptide released by the central nervous system. A number of studies have demonstrated the role of this neuropeptide in the pathogenesis of breast cancer. In the present project, we have identified mRNA coding genes and long non-coding RNAs (lncRNAs) that are associated with this pathway through an in-silico strategy, and measured their expression in a cohort of Iranian females affected with this type of malignancy. Expression levels of OXTR, FOS, ITPR1, RCAN1, CAMK2D, CACNA2D and lnc_ZFP161 were significantly down-regulated in breast cancer tissues compared with nearby non-cancerous tissues. On the other hand, expression of lnc_MTX2 was higher in breast cancer tissues compared with controls. Expression of lnc_TNS1 and lnc_FOXF1 were not different between these two kinds of samples. Expression of CACNA2D was associated with mitotic rate and PR status (P values = 3.02E-02 and 2.53E-02, respectively). Expression of other oxytocin-related genes was not associated with clinicopathological parameters. FOS and ITPR1 had the highest AUC value among the oxytocin-related genes. Combination of expression profiles of all oxytocin-related genes increased the AUC value to 0.75. However, the combinatorial sensitivity and specificity values were lower than some individual genes. In the breast cancer tissues, the most robust correlations have been detected between lnc_ZFP161/ lnc_FOXF1, CAMK2D/ lnc_ZFP161 and CAMK2D / lnc_FOXF1 (r = 0.86, 0.71 and 0.64 respectively). In the non-cancerous tissues, the strongest correlation was detected between lnc_FOXF1/lnc_MTX2 and lnc_ZFP161/CAMK2D respectively (r = 0.78 and 0.65). Taken together, oxytocin-associated genes have been dysregulated in breast cancer tissues. Moreover, the correlation ratio between these genes is connected with the existence of cancer.

Assessment of the calcium releasing machinery in oocytes that failed to fertilize after conventional ICSI and assisted oocyte activation.

RESEARCH QUESTION: Can oocyte-related activation deficiencies be evaluated in oocytes that failed to fertilize after intracytoplasmic sperm injection (ICSI) combined with assisted oocyte activation (AOA)? DESIGN: Evaluation of the spindle-chromosome complexes and intracellular distribution of inositol trisphosphate type 1 receptors (IP3R1) in in-vitro matured (IVM) and failed-to-fertilize oocytes from patients undergoing AOA. Assessment of the oocyte-related Ca2+ releasing capacity in response to Ca2+ ionophores and sperm microinjection in oocytes that failed to fertilize after ICSI or ICSI-AOA. RESULTS: IVM oocytes from patients undergoing conventional ICSI (control) and ICSI-AOA (study group) revealed a similar normalcy of spindle-chromosome complexes and distribution patterns of IP3R1. Failed-to-fertilize oocytes from both groups showed significant differences in proportion of normal or abnormal spindle-chromosome complex conformations. However, migration of IP3R1 was identified in a higher proportion of failed-to-fertilize oocytes after ICSI-AOA than after conventional ICSI. It was further observed that oocytes which failed to fertilize, either after ICSI or ICSI-AOA, mostly retain their capacity to respond to stimuli such as exposure to Ca2+ ionophores or to sperm microinjection. CONCLUSIONS: Evaluation of spindle-chromosome normalcy and distribution of IP3R1 does not help identify the presence of Ca2+ releasing deficiencies in these oocytes. However, oocyte Ca2+ analysis adds value in identifying Ca2+ releasing incapacity of oocytes that failed to fertilize after ICSI or ICSI-AOA. Some patients experiencing fertilization failure after ICSI-AOA present with a suspected activation deficiency downstream of the Ca2+ machinery, which cannot be overcome by ICSI-AOA based on the use of Ca2+ ionophores.

Mapping Interpuff Interval Distribution to the Properties of Inositol Trisphosphate Receptors.

Tightly clustered inositol trisphosphate receptors (IP3Rs) control localized Ca2+ liberation from the endoplasmic reticulum to generate repetitive Ca2+ puffs. Distributions of the interpuff interval (IPI), i.e., the waiting time between successive puffs, are found to be well characterized by a probability density function involving only two parameters, λ and ξ, which represent the basal rate of puff generation and the recovery rate from refractoriness, respectively. However, how the two parameters depend on the kinetic parameters of single IP3Rs in a cluster is still unclear. In this article, using a stochastic puff model and a single-channel data-based IP3R model, we establish the dependencies of λ and ξ on two important IP3R model parameters, IP3 concentration ([IP3]) and the recovery rate from Ca2+ inhibition (rlow). By varying [IP3] and rlow in physiologically plausible ranges, we find that the ξ-λ plane is comprised of only two disjoint regions, a biologically impermissible region and a region where each parameter set (ξ, λ) can be caused by using two different combinations of [IP3] and rlow. The two combinations utilize very different mechanisms to maintain the same IPI distribution, and the mechanistic difference provides a way of identifying IP3R kinetic parameters by observing properties of the IPI.

Mesoscopic behavior from microscopic Markov dynamics and its application to calcium release channels.

A major challenge in biology is to understand how molecular processes determine phenotypic features. We address this fundamental problem in a class of model systems by developing a general mathematical framework that allows the calculation of mesoscopic properties from the knowledge of microscopic Markovian transition probabilities. We show how exact analytic formulae for the first and second moments of resident time distributions in mesostates can be derived from microscopic resident times and transition probabilities even for systems with a large number of microstates. We apply our formalism to models of the inositol trisphosphate receptor, which plays a key role in generating calcium signals triggering a wide variety of cellular responses. We demonstrate how experimentally accessible quantities, such as opening and closing times and the coefficient of variation of inter-spike intervals, and other, more elaborated, quantities can be analytically calculated from the underlying microscopic Markovian dynamics. A virtue of our approach is that we do not need to follow the detailed time evolution of the whole system, as we derive the relevant properties of its steady state without having to take into account the often extremely complicated transient features. We emphasize that our formulae fully agree with results obtained by stochastic simulations and approaches based on a full determination of the microscopic system's time evolution. We also illustrate how experiments can be devised to discriminate between alternative molecular models of the inositol trisphosphate receptor. The developed approach is applicable to any system described by a Markov process and, owing to the analytic nature of the resulting formulae, provides an easy way to characterize also rare events that are of particular importance to understand the intermittency properties of complex dynamic systems.

Automated maximum likelihood separation of signal from baseline in noisy quantal data.

Data recordings often include high-frequency noise and baseline fluctuations that are not generated by the system under investigation, which need to be removed before analyzing the signal for the system's behavior. In the absence of an automated method, experimentalists fall back on manual procedures for removing these fluctuations, which can be laborious and prone to subjective bias. We introduce a maximum likelihood formalism for separating signal from a drifting baseline plus noise, when the signal takes on integer multiples of some value, as in ion channel patch-clamp current traces. Parameters such as the quantal step size (e.g., current passing through a single channel), noise amplitude, and baseline drift rate can all be optimized automatically using the expectation-maximization algorithm, taking the number of open channels (or molecules in the on-state) at each time point as a hidden variable. Our goal here is to reconstruct the signal, not model the (possibly highly complex) underlying system dynamics. Thus, our likelihood function is independent of those dynamics. This may be thought of as restricting to the simplest possible hidden Markov model for the underlying channel current, in which successive measurements of the state of the channel(s) are independent. The resulting method is comparable to an experienced human in terms of results, but much faster. FORTRAN 90, C, R, and JAVA codes that implement the algorithm are available for download from our website.

A bead aggregation assay for detection of low-affinity protein-protein interactions reveals interactions between N-terminal domains of inositol 1,4,5-trisphosphate receptors.

Interactions between proteins are a hallmark of all cellular activities. Such interactions often occur with low affinity, a feature that allows them to be rapidly reversible, but it makes them difficult to detect using conventional methods such as yeast 2-hybrid analyses, co-immunoprecipitation or analytical ultracentrifugation. We developed a simple and economical bead aggregation assay to study low-affinity interactions between proteins. By coating beads with interacting proteins, the weak interactions between many proteins are sufficient to allow stable aggregation of beads, an avidity effect. The aggregation is easily measured to allow quantification of protein-protein interactions under a variety of controlled conditions. We use this assay to demonstrate low-affinity interactions between the N-terminal domains of an intracellular Ca(2+) channel, the type 1 inositol 1,4,5-trisphosphate receptor. This simple bead aggregation assay may have widespread application in the study of low-affinity interactions between macromolecules.

A kinetic model for type I and II IP3R accounting for mode changes.

Based upon an extensive single-channel data set, a Markov model for types I and II inositol trisphosphate receptors (IP(3)R) is developed. The model aims to represent accurately the kinetics of both receptor types of IP(3)R depending on the concentrations of inositol trisphosphate (IP(3)), adenosine trisphosphate (ATP), and intracellular calcium (Ca(2+)). In particular, the model takes into account that for some combinations of ligands the IP(3)R switches between extended periods of inactivity alternating with intervals of bursting activity (mode changes). In a first step, the inactive and active modes are modeled separately. It is found that, within modes, both receptor types are ligand-independent. In a second step, the submodels are connected by transition rates. Ligand-dependent regulation of the channel activity is achieved by modulating these transitions between active and inactive modes. As a result, a compact representation of the IP(3)R is obtained that accurately captures stochastic single-channel dynamics including mode changes in a model with six states and 10 rate constants, only two of which are ligand-dependent.

MCMC estimation of Markov models for ion channels.

Ion channels are characterized by inherently stochastic behavior which can be represented by continuous-time Markov models (CTMM). Although methods for collecting data from single ion channels are available, translating a time series of open and closed channels to a CTMM remains a challenge. Bayesian statistics combined with Markov chain Monte Carlo (MCMC) sampling provide means for estimating the rate constants of a CTMM directly from single channel data. In this article, different approaches for the MCMC sampling of Markov models are combined. This method, new to our knowledge, detects overparameterizations and gives more accurate results than existing MCMC methods. It shows similar performance as QuB-MIL, which indicates that it also compares well with maximum likelihood estimators. Data collected from an inositol trisphosphate receptor is used to demonstrate how the best model for a given data set can be found in practice.

Calcium signals driven by single channel noise.

Usually, the occurrence of random cell behavior is appointed to small copy numbers of molecules involved in the stochastic process. Recently, we demonstrated for a variety of cell types that intracellular Ca2+ oscillations are sequences of random spikes despite the involvement of many molecules in spike generation. This randomness arises from the stochastic state transitions of individual Ca2+ release channels and does not average out due to the existence of steep concentration gradients. The system is hierarchical due to the structural levels channel--channel cluster--cell and a corresponding strength of coupling. Concentration gradients introduce microdomains which couple channels of a cluster strongly. But they couple clusters only weakly; too weak to establish deterministic behavior on cell level. Here, we present a multi-scale modelling concept for stochastic hierarchical systems. It simulates active molecules individually as Markov chains and their coupling by deterministic diffusion. Thus, we are able to follow the consequences of random single molecule state changes up to the signal on cell level. To demonstrate the potential of the method, we simulate a variety of experiments. Comparisons of simulated and experimental data of spontaneous oscillations in astrocytes emphasize the role of spatial concentration gradients in Ca2+ signalling. Analysis of extensive simulations indicates that frequency encoding described by the relation between average and standard deviation of interspike intervals is surprisingly robust. This robustness is a property of the random spiking mechanism and not a result of control.

A kinetic model of the inositol trisphosphate receptor based on single-channel data.

In many cell types, the inositol trisphosphate receptor is one of the important components controlling intracellular calcium dynamics, and an understanding of this receptor is necessary for an understanding of calcium oscillations and waves. Based on single-channel data from the type-I inositol trisphosphate receptor, and using a Markov chain Monte Carlo approach, we show that the most complex time-dependent model that can be unambiguously determined from steady-state data is one with three closed states and one open state, and we determine how the rate constants depend on calcium. Because the transitions between these states are complex functions of calcium concentration, each model state must correspond to a group of physical states. We fit two different topologies and find that both models predict that the main effect of [Ca(2+)] is to modulate the probability that the receptor is in a state that is able to open, rather than to modulate the transition rate to the open state.

Calcium-dependent inactivation and the dynamics of calcium puffs and sparks.

Localized intracellular Ca(2+) elevations known as puffs and sparks arise from the cooperative activity of inositol 1,4,5-trisphosphate receptor Ca(2+) channels (IP(3)Rs) and ryanodine receptor Ca(2+) channels (RyRs) clustered at Ca(2+) release sites on the surface of the endoplasmic reticulum or sarcoplasmic reticulum. When Markov chain models of these intracellular Ca(2+)-regulated Ca(2+) channels are coupled via a mathematical representation of a Ca(2+) microdomain, simulated Ca(2+) release sites may exhibit the phenomenon of "stochastic Ca(2+) excitability" reminiscent of Ca(2+) puffs and sparks where channels open and close in a concerted fashion. To clarify the role of Ca(2+) inactivation of IP(3)Rs and RyRs in the dynamics of puffs and sparks, we formulate and analyze Markov chain models of Ca(2+) release sites composed of 10-40 three-state intracellular Ca(2+) channels that are inactivated as well as activated by Ca(2+). We study how the statistics of simulated puffs and sparks depend on the kinetics and dissociation constant of Ca(2+) inactivation and find that puffs and sparks are often less sensitive to variations in the number of channels at release sites and strength of coupling via local [Ca(2+)] when the average fraction of inactivated channels is significant. Interestingly, we observe that the single channel kinetics of Ca(2+) inactivation influences the thermodynamic entropy production rate of Markov chain models of puffs and sparks. While excessively fast Ca(2+) inactivation can preclude puffs and sparks, moderately fast Ca(2+) inactivation often leads to time-irreversible puffs and sparks whose termination is facilitated by the recruitment of inactivated channels throughout the duration of the puff/spark event. On the other hand, Ca(2+) inactivation may be an important negative feedback mechanism even when its time constant is much greater than the duration of puffs and sparks. In fact, slow Ca(2+) inactivation can lead to release sites with a substantial fraction of inactivated channels that exhibit puffs and sparks that are nearly time-reversible and terminate without additional recruitment of inactivated channels.

Modeling local and global intracellular calcium responses mediated by diffusely distributed inositol 1,4,5-trisphosphate receptors.

Considerable insight into intracellular Ca2+ responses has been obtained through the development of whole cell models that are based on molecular mechanisms, e.g., single channel kinetics of the inositol 1,4,5-trisphosphate (IP3) receptor Ca2+ channel. However, a limitation of most whole cell models to date is the assumption that IP3 receptor Ca2+ channels (IP3Rs) are globally coupled by a "continuously stirred" bulk cytosolic [Ca2+], when in fact open IP3Rs experience elevated "domain" Ca2+ concentrations. Here we present a 2N+2-compartment whole cell model of local and global Ca2+ responses mediated by N=100,000 diffusely distributed IP3Rs, each represented by a four-state Markov chain. Two of these compartments correspond to bulk cytosolic and luminal Ca2+ concentrations, and the remaining 2N compartments represent time-dependent cytosolic and luminal Ca2+ domains associated with each IP3R. Using this Monte Carlo model as a starting point, we present an alternative formulation that solves a system of advection-reaction equations for the probability density of cytosolic and luminal domain [Ca2+] jointly distributed with IP3R state. When these equations are coupled to ordinary differential equations for the bulk cytosolic and luminal [Ca2+], a realistic but minimal model of whole cell Ca2+ dynamics is produced that accounts for the influence of local Ca2+ signaling on channel gating and global Ca2+ responses. The probability density approach is benchmarked and validated by comparison to Monte Carlo simulations, and the two methods are shown to agree when the number of Ca2+ channels is large (i.e., physiologically realistic). Using the probability density approach, we show that the time scale of Ca2+ domain formation and collapse (both cytosolic and luminal) may influence global Ca2+ oscillations, and we derive two reduced models of global Ca2+ dynamics that account for the influence of local Ca2+ signaling on global Ca2+ dynamics when there is a separation of time scales between the stochastic gating of IP3Rs and the dynamics of domain Ca2+.

Calcium release site ultrastructure and the dynamics of puffs and sparks.

When Markov chain models of intracellular Ca(2+)-regulated Ca(2+) channels are coupled via a mathematical representation of a Ca(2+) microdomain, simulated Ca(2+) release sites may exhibit the phenomenon of 'stochastic Ca(2+) excitability' reminiscent of Ca(2+) puffs and sparks. Interestingly, some single-channel models that include Ca(2+) inactivation are not particularly sensitive to channel density, so long as the requirement for inter-channel communication is satisfied, while other single-channel models that do not include Ca(2+) inactivation open and close synchronously only when the channel density is in a prescribed range. This observation led us to hypothesize that single-channel models with Ca(2+) inactivation would be less sensitive to the details of release site ultrastructure than models that lack a slow Ca(2+) inactivation process. To determine if this was the case, we simulated Ca(2+) release sites composed of instantaneously coupled Ca(2+)-regulated Ca(2+) channels whose random spatial locations were chosen from a uniform distribution on a disc of specified radius and compared the resulting release site dynamics to simulations with channels arranged on hexagonal lattices. Analysis of puff/spark statistics confirmed our hypothesis that puffs and sparks are less sensitive to the spatial organization of release sites when the single-channel model includes a slow inactivation process. We also investigated the validity of several different mean-field reductions that do not explicitly account for the details of release site ultrastructure. The most successful approximation maintains a distinction between each channel's substantial influence on its own stochastic gating and the collective contribution of elevated [Ca(2+)] from neighbouring channels.

A kinetic Monte Carlo simulation study of inositol 1,4,5-trisphosphate receptor (IP3R) calcium release channel.

Most of the previously theoretical studies about the stochastic nature of the IP3R calcium release channel gating use the chemical master equation (CME) approach. Because of the limitations of this approach we have used a stochastic simulation algorithm (SSA) presented by Gillespie. A single subunit of De Young-Keizer (DYK) model was simulated using Gillespie algorithm. The model has been considered in its complete form with eight states. We investigate the conditions which affect the open state of the model. Calcium concentrations were the subject of fluctuation in the previous works while in this study the population of the states is the subject of stochastic fluctuations. We found out that decreasing open probability is a function of Ca(2+) concentration in fast time domain, while in slow time domain it is a function of IP3 concentration. Studying the population of each state shows a time dependent reaction pattern in fast and medium time domains (10(-4) and 10(-3)s). In this pattern the state of X(010) has a determinative role in selecting the open state path. Also, intensity and frequency of fluctuations and Ca(2+) inhibitions have been studied. The results indicate that Gillespie algorithm can be a better choice for studying such systems, without using any approximation or elimination while having acceptable accuracy. In comparison with the chemical master equation, Gillespie algorithm is also provides a wide area for studying biological systems from other points of view.