In this paper, we develop an ecological, multi-level model that can be used to study the evolution of emerging technology. More specifically, by defining technology as a system composed of a set of interacting components, we can build upon the argument of multi-level density dependence from organizational ecology to develop a distribution-independent model of technological evolution. This allows us to distinguish between different stages of component development, which provides more insight into the emergence of stable component configurations, or dominant designs. We validate our hypotheses in the biotechnology industry by using patent data from the USPTO from 1976 to 2003. In our industrial and post-industrial societies, technology plays a highly important role, structuring the relationship between individuals, groups, organizations, and industries , 2, 3, 4In this paper, we argue that by considering the multi-level nature of technology, we can generate valuable insights into the growth and evolution of emerging technologies. As technology is inherently multi-level in nature , we concThe contribution of this paper is threefold. First, by conceiving technology as a system that is composed of a set of interacting technological components, we develop a multi-level model that can be used to study the evolution of (emerging) technology. This allows us to examine the microeconomic underpinnings of an emerging technology’s growth and development, and explain how stable patterns of growth and development originate from the bottom up. Second, by applying concepts from organizational ecology to the study of technology, we effectively combine evolutionary and ecological models in the study of the evolution of technology (cf. ). That iThe organization of this paper is as follows. In the next section, we will discuss the literature on the evolution and, on the basis of these insights, develop our hypotheses. Subsequently, in the ‘Data and methodology’ section, we outline our empirical sample and estimation methods. We then discuss our results, and finally, in the ‘Discussion and conclusion’, we discuss the limitations of our study, position our findings in the broader academic debate, and provide some thoughts on avenues for further research.Even though economists already recognized the importance of technological progress early on , they geEspecially the field of evolutionary economics has contributed much to our understanding of the process of technological change. According to evolutionary economics, due to an agent’s limits in information-processing and problem-solving capacity, or bounded rationality , there iAt the organizational level, these heuristics translate into organizational routines that ensure regular and predictable patterns of behavior  that resWhile these stable and predictable technological design configurations lead to stable and predictable patterns of technological growth, they do not emerge ex nihilo, implying at least two stages of technological evolution. These two stages of technological evolution have also been documented extensively in the literature. For example, according to Utterback and Abernathy , technolHence, technological growth and evolution is characterized by two stages of development. Initially, during the stage of emergence, through a process of social construction , stakehoThe stable and predictable patterns of technological growth that characterize mature technologies are much better suited for formal modeling than the chaotic patterns of change that are associated with emerging technologies. As we will explain below, formal models that are tailored to study the growth and evolution of mature technologies are not well-suited to study emerging technologies. Although emerging technologies are studied indirectly as part of mature technologies, this is only done in retrospect, which severely limits our understanding of the growth and evolution of emerging technology. When studying diffusion processes and population evolution prospectively , better and more unbiased insights result .In what is considered as one of the first scientific works that treats the development of new technology as an economic phenomenon, Zvi Griliches  found thAlternatives of the Pearl Reed/Logistics curve have also been proposed, such as an exponential  and GompAnother well-known model is Bass’  rate of We believe that the main reason that these models are not well-suited for studying emerging technologies is that they do not consider the multi-level nature of technology. As the phenomenon of technology is inherently multi-level in character , developIn the current study, we develop a formal model to study the growth and evolution of emerging technology by explicitly acknowledging the hierarchical nature of technology. In nature and society, many things are organized hierarchically , and tecObviously, emerging technologies do not always display highly chaotic and unstable patterns of growth and development, as the stable design (or component) configurations that characterize mature technologies are  constructed during the stage of emergence. For example, in The approach we take in the current study is to build upon insights from organizational ecology, a prominent sociological theory of the evolution of organizations and organizational populations. The reason for doing so is that the theory of density dependence is highly developed in organizational ecology. As explained above, density dependence characterizes most existing models of technological growth and development. This means that we can build upon density dependence theory from organizational ecology to build a model of emerging technologies. Organizational ecology’s density dependence theory argues that population density serves as a surrogate for the difficult-to-observe features of the material and social environment that affect population dynamics, particularly social legitimation  and diffuse competition . Even though density dependence logic has been primarily applied to organizational populations , recent Given the multi-level nature of our model, we build upon multi-level density dependence theory . This thseed stage of component development. Then, as stakeholders recombine the focal component with the other components, its role in the system becomes apparent, and the component is legitimated at the system level . Now, the component forms an integral part of the technological system, and legitimation processes at the system level start to contribute positively to component growth. We refer to this stage as the growth stage of component development. This effectively allows us to study the evolution of emerging technologies by investigating the evolution of their principal components. Accordingly, we develop several hypotheses regarding component evolution next.The component-system interaction of an emerging technology  also implies (at least) two stages of development at the component level. Initially, when a focal component is first discovered, it is unclear how this component relates to the other components (if any) of the system, implying that the component is not legitimated at the system level. In line with the different stages of the technology life cycle see , we refeAs mentioned, we will use the effect of the density of the technological system  to distinguish between two stages of component growth . As such, we cannot develop any hypotheses about the effect of system density on the development and evolution of its component technologies. Hence, our first hypothesis explores the effect of the density of the component on its own development and evolution. For this hypothesis, we will also build on organizational ecology’s density dependence theory, by considering the technological components as populations of related technological inventions. To clarify, let us illustrate this with an example. Imagine automobile technology as the technological system. This technological system is comprised of several technological components, such as the engine, drive train, brake system, wheels, cooling system, body, et cetera. Each of these component technologies were not invented out of nothing, but are actually comprised of populations of related inventions, as scientists and engineers built upon inventions from their predecessors to develop our modern-day internal combustion engine.As said, density dependence theory argues that population density can be used as a surrogate for the difficult-to-observe features of the material and social environment that affect population dynamics; i.e., social legitimation and diffuse competition. The theory also posits that legitimation is mainly important in the formative stages of population development, while competition is mainly important in the mature stage of population development , 51. As Hypothesis 1: Component density is positively associated with component development, and is not significantly lower in the growth stage of development versus the seed stage of component development.Nowadays, most technology is developed in the context of organizations. This means that organizational density plays a highly important role in the development of technology, and that the legitimation of technology is tied to the number of organizations adopting the technology , 63, 64.Hypothesis 2: Organizational density is positively associated with component growth, and more strongly so in the seed stage of development.Conceiving technological change as a process of recombination naturally implies technological lineage, where a focal invention builds upon antecedent inventions, and can subsequently become the basis for future descendant inventions , 67. ThiComponent diversity represents the degree to which technological development within the component takes place within different subcomponents. This has different implications in different stages of technological development. On the one hand, in the seed stage of component development, more subcomponents imply more alternative subcomponent configurations, which is associated with more flexibility that prevents lock-in or path dependence , 73. In On the other hand, in the growth stage of development, the potential appeal of the component’s technology to stakeholders in the wider technological environment is no longer relevant. After all, the component’s stakeholders have collectively agreed upon working on the dominant subcomponent configuration, specified in the component’s dominant design. This enables actor specialization and facilitates cumulative development, thus transforming the component’s potential value into real techno-economic value. In doing so, a (broad) direction of technological development for the component has been chosen, and alternative directions  have been foreclosed. Hence, diversity  implies a fragmentation of resources, and hampers convergence towards the component’s stable subcomponent configuration. This suggests:Hypothesis 3: Component diversity is positively (negatively) associated with component growth in the seed (growth) stage of development.The antecedent technology of a component can be seen as the knowledge base on which the component builds. Because the development of the technological component is to a large extent dependent on the underlying knowledge base , the morConversely, before a dominant subcomponent configuration or sense-making structure does emerge, stakeholders need to literally consider all potential (re-)combinations, as they can all become the basis of the future dominant design. Increasing the diversity of the knowledge base in this (seed) stage actually diminishes possibilities for sense-making and absorption, and yields high integration costs. Hence, in the seed stage of component development, antecedent diversity results in a fragmentation of resources and a duplication of research efforts. This logic provides:Hypothesis 4: Antecedent diversity is negatively (positively) associated with component growth in the seed (growth) stage of development.The technological descendants of the focal niche can be seen as the technological extensions of the component’s technology, or its application domain in the broader technological environment or landscape. The more diverse its application domain, the more the component’s technology is diffused, and the more attractive the component technology becomes for potential adopters . After aHypothesis 5: Descendant diversity is positively associated with component growth.Within innovation and technology studies, there is a long history of using patent data , 79, 80.For many ‘dedicated biotechnology firms’, or DBFs, a common strategy is to patent and subsequently license out or sell their technological knowledge. Large pharmaceutical firms also use patents strategically–for example, as leverage or bargaining chips in negotiations, or to stifle developments by competitors. Since the Bayh-Doyle act–which allows the patenting of research findings funded by means of federal grants–research institutes, such as universities, are also highly active to patent their newly discovered technology. As a result, all landmark innovations within biotechnology have been patented. Many scholars therefore believe that patent data are well suited to delineate different stages of technological evolution and the characteristics of the technological (selection) environment in this domain. Although we acknowledge that patent data only represent the explicit portion of technological knowledge contained within an organization or industry, due to the importance of patents within biotechnology, it is also a fair proxy for the tacit portion of technological knowledge. Especially within biotechnology, patents form a reliable indicator of technological developments , 86, 87.We use patent data from the United States Patent and Trademark Office (USPTO), as this is the most complete dataset for technology analysis , 89. BecComponent growth, our dependent variable, is a count of the number of patents that enter a focal technological component in a particular month from January 1976 until December 2003. Because we have repeated observations for the same components, our data constitute a cross-sectional time-series or panel data structure. This panel is unbalanced, though, as not all components were in existence at the start of our time window.Regarding our density measures, System density is a count of the total number of patents (in thousands) contained within the whole domain of biotechnology in the month prior to our dependent variable. To avoid double counting, we have subtracted focal component density. Next, focal Component density is a count of the total number of patents (in thousands) in the focal component in the month prior to the dependent variable. This measure represents the stock of patents contained in the focal component at any given time. Finally, Organizational density is a count of the number of organizations active in the component in the previous twelve months in thousands.Dit denotes to the diversity of component i at time t, and Pijt is the share of patents in subcomponent j at time t in component i, time t refers to the twelve-month period prior to the month of observation of our dependent variable, and J denotes the number of subcomponents associated with the component.Component diversity represents the extent to which developments take place in different subcomponents, and is measured by the distribution of patents across all USPTO sub-classes over the previous twelve months. To measure component diversity, we use Shannon’s  diversitPijt refers to the share of citations made from focal component j to antecedent component i at time t. Correspondingly, the component's Descendant diversity is calculated over the previous twelve months using (1), where Pijt is the share of citations received from descendant component i to focal component j at time t.The component’s Antecedent diversity is calculated over the previous twelve months according to (1), but now 2 –to allow for the estimation of dynamic models [We also add a number of control variables. First, we include the number of previous entries and its square–Previous entry and Previous entryc models . This pac models , 93. We As can be seen in Y refers to our dependent variable , Ss to system density in the seed stage, Sg to system density in the growth stage, X to variables whose coefficient values are not expected to change, β to the coefficient vector, and ε to the associated error term.To distinguish between different stages of component evolution, we employ a structural break model. As the name implies, a structural break model allows an investigation of whether a time series features a structural break, which means that there is a significant change in the effects of the model between two (or more) periods . The genλit is the deterministic function of covariates.By estimating this structural break model for each of our technological components individually, we are able to determine at which point in time system density has a significant positive effect on component entry that marks the beginning of the growth stage of our components. The dependent variable for this model is component growth, as reflected by the entry of inventions into our components, which is a count variable. The baseline model for analyzing count data is the Poisson distribution. After adding covariates to the distribution, this gives the Poisson regression model. However, the restriction of applying the Poisson distribution in linear regression analysis is that, after adding covariates, the sample mean and conditional variance of the dependent variable have to be equal . BecauseOur main covariate is system density, which we split on a yearly basis into a before  and after  part to determine the location of the structural break, if present. For example, for the year 1980, we distinguish between system density before 1/1/1980  and system density after 1/1/1980 , to then investigate the coefficient values of the before and after part of system density. We accept the structural break as the distinction between the seed and growth stage if (1) we find a non-significant effect for ‘before system density’–that is, system density in the seed stage of development–and (2) we find a significant positive effect for ‘after system density’–that is, system density in the growth stage of development. In the case of multiple valid options, we select the breakpoint with the higher Log-Likelihood value .Ot is organizational density at time t, Ct is component density at time t, Sst is system density in the seed stage of development at time t, and Sgt is system density in the growth stage of development at time t.We add several controls. First, we include the previous number of entries and its square as a control for favorable conditions , 93. We r and s). This yields the modelAfter delineating the different stages of component development, we can continue to test our hypotheses. Our data involves monthly observations from 1976 to 2003 for 27 technological components, so we are dealing with a cross-sectional time-series or panel data structure. When modeling panel data, there are basically two options: employing a dynamic regression model or a panel regression model. We already apply a dynamic regression model because we include the previous occurrences of our dependent variable  in our analysis. We also employ a panel regression model, which comes in two basic flavors: (1) a fixed effects model, which adds a dummy per panel, and so effectively removes all variance between panels–also referred to as a within-variance model; and (2) a random effects model, which assumes that heterogeneity is randomly distributed and therefore makes it possible to utilize both the within- and between-panel variance. More specifically, according to Hausman, Hall, and Griliches , the ranδi is allowed to vary randomly across groups, and 1/(1+ δi) ~ Beta. As the name already indicates, the random effects model has the limiting assumption that the unobserved heterogeneity is randomly distributed, and therefore independent from the regressors . To determine whether this is indeed the case, we apply Hausman’s [In the random effects overdispersion model, ausman’s  specificOur data involve left-censoring, as data are missing for the beginning of the history of our population (patent citations are missing for the pre-1976 period). However, this does not imply a survivor bias, as we have all cohorts. Furthermore, while scientific developments in biotechnology were surely stimulated after the discovery of DNA in 1972, commercial activity only took off after 1980 [χ2 distribution with one degree of freedom (column 8 of Sg in p < .05).In th highest of all components, and (3) maximum component entry is 14 components per month, which is 12th highest of all components. The other components  are assumed to be in the seed or formative stage during the observation period. To determine the performance of our multi-level structural break model, we have also estimated different stages of component development using a Bass [For the remaining components, we did not find a structural break on the basis of our criteria. This implies that these components are in the same stage of development for the whole period of observation. The question then becomes whether they are in the seed or growth stage of development during our time window. Again, on the basis of the effect of system density on component entry, we can determine the appropriate stage. As can be seen in To determine whether we are indeed observing different stages of technological development for our components, we first have a look at the confidence interval of our dependent variable . As can be seen in We have performed Hausman’s specification test, revealing that a random effects specification is indeed appropriate, as our independent variables are not correlated with the random disturbance term.st quartile to its 3rd quartile increases niche entry with 216% (136%). At first sight, it might appear that component density has a different effect in the different stages of component development. However, this is mainly due to the different value ranges of component density in the different stages of development. To determine whether the effect of density is significantly weaker in the growth stage of development, we have estimated a structural break model following (2) where component density was split into its seed and growth part. According to this analysis, the effect of component density is not significantly lower in the growth stage of development, thus providing support for our hypothesis.Hypothesis 1 posits that, following density dependence logic, component density is positively associated with component growth in both stages of technological development, and that this effect is not significantly weaker in the growth stage of development. We find strong support for this hypothesis. In the seed (growth) stage of development, moving niche density from its 1st to the 2nd quartile in the growth stage of development, increases component growth with 71%; moving organizational density from its median value to the 3rd quartile, further increases component growth with 85% (236%) in the growth (seed) stage of development.Hypothesis 2 implies the assumption that the number of organizations that adopt the component technology is a proxy for the legitimation of technology. Because legitimation processes are more important in the formative (seed) stage of development, a stronger positive effect is expected in this stage. Our estimates provide some, but not full support for this hypothesis. We do find a stronger effect for the linear term of organizational density in the seed stage of development, which means that the effect of organizational density is stronger in the seed stage than in the growth stage. However, additional analyses  do not reveal a significant difference in the two functions. Even though the coefficients are not significantly different, the difference in effects is rather large. Moving organizational density from the 1Hypothesis 3 argues that niche diversity is positively (negatively) associated with component entry in the seed (growth) stage of development. This hypothesis is fully confirmed by our estimates. In the seed (growth) stage of development, a standard deviation increase in niche diversity increases (decreases) niche entry with 95% (20%).Hypothesis 4 states that antecedent diversity is negatively (positively) associated with component entry in the seed (growth) stage of technological development. We only find partial support for this hypothesis. On the one hand, although the effect of antecedent diversity is positive in the growth stage of development, this effect is non-significant. On the other hand, antecedent diversity does have a significant negative effect on component growth in the seed stage, and increasing antecedent diversity with one standard deviation decreases component entry with 34%. Additional analyses (available upon request) also reveal that the coefficients of antecedent diversity are significantly different in the two stages of development.Finally, Hypothesis 5, which postulates that descendant diversity has a positive effect in both stages of development, can also be confirmed by our estimates. Increasing the value of descendant diversity with one standard deviation in the seed (growth) stage of development, increases niche entry with 27% (16%). Further analysis indicates that there is no significant difference in the coefficient value for descendant diversity in the two stages of development.Overall, our estimates generate broad support for many of our hypotheses, providing evidence for our claim that there are indeed different stages of technological evolution at the component level, each associated with specific stage-dependent evolutionary processes. This allows us to move beyond a mere caricature of technological evolution as an S-shaped growth pattern , and enaBy conceiving of technology as a system composed of a set of interdependent components that evolve through different stages of technological development, we have effectively created a multi-level and systemic evolutionary model of technological growth. According to our model, when a component develops a stable role within a technological system , it begins to form an integral part of that system, and the processes that direct the component’s growth and evolution change. On the one hand, before the emergence of this dominant subcomponent configuration , focal diversity has a positive effect on component growth. Hence, alternative subcomponent configurations attract resources and attention to further the development of this array of alternative technological structures . However, despite the advantageous effect of alternative technological structures , these alternative configurations should not be based upon diverse technological knowledge, which is indicated by the negative effect of antecedent diversity in this stage. That is, due to the lack of a dominant subcomponent design, diverse knowledge reduces the sense-making capabilities and increases integration costs.On the other hand, after a dominant design has been established, the component is legitimated at the system level, which means that it now forms an integral part of the system’s structure. This means that alternative subcomponent configurations  thwart resources and attention from the agreed-upon dominant subcomponent configuration, which hampers technological growth. Moreover, because the dominant design provides a means to make sense of the environment, knowledge-base diversity no longer has a negative effect on technological growth. The dominant subcomponent configuration  acts as some sort of filter or heuristic, which not only redirects resources and attention in the technological environment, but also acts as a sense-making structure with which to interpret the environment. Hence, stability is needed to make sense of the world, hereby reducing uncertainty, enabling specialization, and facilitating cumulative changes.When taking this previous argument to a higher level of aggregation, after each component is identified and has established a stable role within the technological system, the system itself becomes a stable and predictable integrated whole, and enters the growth stage of development. That is, the system has formed a dominant component configuration, or a dominant system design. In other words, stability travels upward. This connects to Barley’s  finding The current literature on technology and innovation treats the origins of novelty as exogenous and random, and focuses mainly on processes of diffusion and absorption , 103. InBy taking into account the lineage of technology in combination with diversity, we found that the embeddedness of a component  has a substantial effect on its growth rate, illustrating the importance of a socialized perspective towards technological change. Both sides of a component’s technological lineage, namely the diversity of its antecedent and of its descendant technologies, have a strong effect on the focal component’s growth rate. As illustrated above, this has generated more insight into the twin processes of knowledge creation and diffusion, characterized by the different stages of technological development. Even though we have demonstrated the significance of the diversity of these dimensions of the technological component, it is also possible to conceive of other characteristics of these dimensions. For example, results from some preliminary analyses indeed indicate that antecedent and descendant stability also play an important role in the evolution and growth of technology. A thorough investigation of the effect of different characteristics of antecedent and descendant technology on technological development would surely contribute much to our understanding of the growth and evolution of technology.In this paper, we have found that an emerging technology’s components are characterized by two stages of development: i.e., the seed and growth stage. We have shown that the different levels of analysis are not independent from one another. More specifically, the growth of component technology not only influences the growth of the system as a whole , but the configuration of components at the system level also influences subsequent component growth . As such, our analysis stresses the crucial role of cross-fertilization at the level of underlying technological components and the existence of an intricate link between different levels of analysis. Actually, we believe that the S-shaped growth pattern characterizes technological populations at multiple levels of analysis, and that the different stages of the technology lifecycle  at different levels of analysis are linked to one another. Obviously, future research is needed to really move deeper ‘inside the black box’ of technology development . Some ofFirst, future research could explore how to draw the boundaries of technological populations at multiple levels of analysis. Ecological theories are typically silent about how boundaries should be drawn . In thisSecond, to determine whether different levels of analysis are indeed characterized by the different stages of the technological lifecycle with intimate links between these stages across different levels of analysis would require studying technologies in their (near) final stages of development: i.e., mature technologies. For example, researchers could study the evolution of semiconductor technology, as this technology is mature and has penetrated much of our society.Third, we implicitly assume that the seed stage of technological development is characterized by competition between alternative component configurations. However, in the current paper, we do not measure this competition in any way. Future research could explore whether processes of competition can indeed be witnessed between alternative component configurations. This implies the need of being able to identify different component configurations during the seed stage of system development. Although this is not an easy task, requiring an intimate understanding of the technological system that is being investigated, this would imply an important next step toward a deeper understanding of processes of technological development and evolution.Fourth, we have established that technological populations are internally differentiated, and have explored how this internal differentiation influences technological growth and development by introducing insights from organizational ecology. An interesting avenue of future research could be to explore whether a further integration of organizational ecological insights would be beneficial. For example, researchers could more explicitly focus on how processes of natural selection affect the growth and evolution of technological populations. Using ecological frameworks, it is possible to study the competition and legitimation between technological populations at multiple levels of analysis. One interesting avenue would be to investigate the process of technological substitution: i.e., the replacement of one technology for another.Fifth, to concentrate our attention on the evolution of technology, we have largely abstracted from the role of the organization. Obviously, the insights from this analysis should be integrated with extant knowledge about organizational evolution, first by means of theoretical exploration, and subsequently by means of empirical investigation. Here, we have opened the door to a plethora of highly interesting research questions that can be further developed and explored. More specifically, by providing a quantitative model that facilitates a distinction between different stages of technological evolution, it becomes possible to explore the consequence of these different stages for individual organizations, by examining the position of organizations within a technological component (or system) at different stages of development, and by relating this position to organizational performance and survival. At higher levels of analyses , we can relate the different stages of technological development to the processes of entry and exit of organizations and organizational forms. This would be an important contribution to the literature. After all, even though it is widely acknowledged that technology drives ecological processes, in population ecology, with few exceptions, connections between technological change and organizational evolution are not of central interest . MoreoveThere is a “considerable and growing interest in the emergence of novel technologies, especially from the policy-making perspective” . The reaTypically, policy-makers want to know which technologies to support. However, one of the biggest problems in technology selection is how to derive promising technology alternatives from blurred and diverging development directions that tend to characterize an emerging technology . On the In firms, technology plays a highly important role and, on average, accounts for more than one-third of all business capital spending . To remaAdditionally, firms can also use the insights from


Tg-resistant ATP-evoked calcium responses in the model macrophage were dependent on extracellular calcium, suggesting a requirement for calcium influx. Ivermectin (IVM) potentiated the magnitude of Tg-resistant component and slowed the decay of response in the model macrophage. The Tg-resistant component was attenuated by P2X4 antagonists 5-BDBD and PSB-12062 but not by the P2X1 antagonist Ro0437626 or the P2X7 antagonist A438079. shRNA-mediated P2X4 knockdown resulted in a significant reduction in Tg-resistant ATP-evoked calcium response as well as reduced sensitivities towards P2X4-specific pharmacological tools, IVM and PSB-12062. Inhibition of endocytosis with dynasore significantly reduced the magnitude of Tg-resistant component but substantially slowed decay response. Inhibition of calcium-dependent exocytosis with vacuolin-1 had no effect on the Tg-resistant component. These pharmacological data suggest that P2X4 receptor activation contributed significantly towards the ionotropic calcium response evoked by ATP of the model human macrophage.Monocytes and macrophages express a repertoire of cell surface P2 receptors for adenosine 5′-triphosphate (ATP) a damage-associated molecular pattern molecule (DAMP), which are capable of raising cytoplasmic calcium when activated. This is achieved either through direct permeation (ionotropic P2X receptors) or by mobilizing intracellular calcium stores (metabotropic P2Y receptors). Here, a side-by-side comparison to investigate the contribution of P2X4 receptor activation in ATP-evoked calcium responses in model human monocytes and macrophages was performed. The expression of P2X1, P2X4, P2X5 and P2X7 was confirmed by qRT-PCR and immunocytochemistry in both model monocyte and macrophage. ATP evoked a concentration-dependent increase in intracellular calcium in both THP-1 monocyte and macrophages. The sarco/endoplasmic reticulum Ca The release of adenosine 5′-triphosphate (ATP) as a signaling molecule by activated leukocytes, platelets and apoptotic cells is crucial for the induction of a variety of physiological responses across different cell types ,2. ATP i2+ response and that its activity is suppressed by cholesterol lowering drug, fluvastatin [Although the role of P2X4 receptors in monocytes and macrophages is not clearly defined within the literature, P2X4 expression has been shown to be upregulated following nerve damage in microglia  and, in vastatin .2+ response, and this was assessed in the two cell models through intracellular Ca2+ measurements. Our findings allowed the identification of P2X4 contribution of towards ATP-evoked Ca2+ influx in model human monocytes and macrophages, while also assessing the reliability of commercially available pharmacological tools for the study of P2X4.In the present study, we have performed a side-by-side comparison of the expression of P2X receptors in model human THP-1 monocytes and macrophages through the use of qRT-PCR and immunocytochemistry. Though evidence of functional P2X4 can be revealed by whole-cell patch-clamps, studies evaluating the role of P2X receptors in intact cells are limited. To this end, we investigated the contribution of P2X4 receptor activation towards ATP-evoked intracellular Can = 5; p < 0.001 and P2X2: 0.13 ± 0.074 fold; n = 5; p < 0.001), while P2X4 were found to be upregulated  in THP-1-differentiated macrophages compared to THP-1 monocytes    peak magnitude was significantly higher in THP-1 monocytes compared to THP-1-differentiated macrophages  A,B and T; n = 6) C,D. Responocytes B,D. Two l number F.2+ response, mediated by both the activation of P2X and P2Y receptors, a sarco/endoplasmic reticulum Ca2+-ATPase inhibitor, Thapsigargin (Tg), was employed to eliminate metabotropic responses. Tg depletes intracellular stores of Ca2+ resulting in a raise in cytosolic Ca2+ level  (n = 6) of the ATP-evoked Ca2+ response appeared to be resistant to Tg. In the absence of extracellular Ca2+, this Tg-resistant Ca2+ response was completely abolished, implying that it is dependent on Ca2+ influx  C,D. Desp+ influx E.2+ response was studied using a combination of pharmacological tools. Pre-treatment of cells with P2X4-specific positive allosteric modulator (ivermectin (IVM); 3 μM) resulted in the potentiation of peak magnitude of Tg-resistant Ca2+ response by 2.1 fold    A,B. In a< 0.001) C. Selectce of Tg B,D. FinaTg-resistant ATP-evoked intracellular Ca2+ level. To provide further confirmation, a molecular-based approach to silence the P2X4 gene was employed. The success of the gene silencing technique was confirmed by the qRT-PCR approach  resulted in a significant reduction of the magnitude of Tg-resistant ATP-evoked Ca2+ response  [2+ response, accompanied by a significant delay in decay kinetics. It has been previously reported that the main characteristics of the IVM effect on P2X4 receptors include potentiation in magnitude and delay in decay current, although studies by Norenberg et al. (2012) illustrated that only the effect on decay kinetics is specific to P2X4 receptors [2+ response was also consistent, in which they both significantly reduced the peak magnitude as well as abolished the second slower response. Despite similar effect in inhibition, PSB-12062 appeared to be more potent in causing an inhibition towards the Tg-resistant Ca2+ response, and this may be due to the nature of the antagonists. While PSB-12062 has been described as an allosteric antagonist [2+ response was confirmed with a knockdown approach, which illustrated that P2X4 knockdown THP-1 differentiated macrophages lacked a Tg-resistant ATP-evoked Ca2+ response as well as reduced sensitivities towards IVM and PSB-12062. As ATP elicits global P2 responses constituting of P2X and P2Y receptors, we utilized a SERCA inhibitor, Thapsigargin (Tg), to deplete the intracellular stores of Caeceptors ,25. Intein (IVM)  and selein (IVM) ,28,29,30eceptors ,28. The 2+ response. Selective P2X1 antagonist Ro0437626 and P2X7 antagonist A438079 had no significant inhibitory effect towards the Tg-resistant ATP-evoked Ca2+ response in THP-1 differentiated macrophages. The lack of contribution of P2X1 in ATP-evoked Ca2+ response in these cells contradict previous studies reported by Sim et al. and Wareham et al. illustrating functional evidence of P2X1 receptor in mouse macrophages [2+ response in THP-1 differentiated macrophages is not as surprising. It is well established that 100 μM ATP used through this study is significantly below the activation threshold for P2X7 receptors (>500 μM) [As P2X1, P2X4 and P2X7 are all expressed in the immune cells ,32, we arophages  and humarophages , respect>500 μM) ,35,36.2+ response, but their functional evidence in THP-1 monocyte is lacking. Studies by Li et al. have previously reported functional P2X4 in THP-1 monocytes, although, in this case, PLC inhibitor U-73122 was used instead of Tg [2+ measurements, we have showed that IVM and PSB-12062 are reliable tools for the study of P2X4, supported with findings from P2X4 knockdown cells. Altogether, the data provides evidence that P2X4 is functionally expressed in THP-1 differentiated macrophages as reflected from their contribution towards ATP-evoked CaThe following reagents were used: PSB-12062 and ATP , A438079 , Ivermectin, 5-BDBD, Ro0437626 and Thapsigargin , and Fura 2-AM .l-glutamine, 10% foetal bovine serum (FBS) and 50 IU/mL penicillin and 50 μg/mL streptomycin. Cells were maintained at 37 °C with 5% CO2 in a humidified incubator. To generate THP-1 differentiated macrophages, cells were stimulated with 320 nM of phorbol 12-myristate 13-acetate (PMA) for 48 h at 37 °C, 5% CO2 in a humidified incubator. THP-1 cells were obtained from the European Collection of Cell Cultures (ECACC). Human THP-1 cells were cultured in Roswell Park Memorial Institute (RPMI) 1640 medium with 2 mM 5/well were loaded for 1 h at 37 °C with 2 μM Fura-2 AM. Cells were washed twice with physiological saline (SBS) buffer. Measurements were performed at 37 °C on a 96-well plate reader . The change in intracellular Ca2+ concentration is indicated as a ratio of fura-2 emission intensities for 340- and 380-nm excitation (F ratio). SBS buffer contained (mM): 130 NaCl, 5 KCl, 1.2 MgCl2, 1.5 CaCl2, 8 d-glucose, 10 HEPES; pH 7.4. Ca2+-free SBS was prepared by excluding CaCl2 and supplemented with 2 mM ethylene glycol-bis(β-aminoethyl ether)-N,N,N߰,N߰-tetraacetic acid (EGTA). Loading of cells with Fura 2-AM was performed in SBS buffer supplemented with 0.01% (w/v) pluronic acid. All compounds were pre-treated onto cells for 30 min before Ca2+ measurements. Cells at 2 × 10Total RNA was isolated from cells using TRI Reagent  and contaminating genomic DNA was eliminated using DNA-free kit . cDNA was synthesized from 0.5 μg of total RNA using SuperScript II reverse transcriptase kit .Ct method.Taqman primer probes sets for human P2X1 (Hs00175686_m1), P2X2 (Hs04176268_g1), P2X3 (Hs01125554_m1), P2X4 (Hs00602442 _m1), P2X5 (Hs01112471_m1), P2X6 (Hs01003997_m1), P2X7 (Hs00175721 _m1), β-actin (Hs01060665_g1) were obtained pre-designed from Applied Biosystems . qRT-PCR was performed in a 7500 Fast Real-Time PCR instrument (Applied Biosystems). Target gene expression was normalized to β-actin endogenous control and relative quantification was done by the ∆∆5) were transduced with either scrambled shRNA or P2X4 shRNA lentiviral particles  in the presence of 8 μg/mL hexadimethrine bromide and Lipofectamine 2000 .THP-1 cells  and permeabilized with 0.25% Triton X-100 . Cells were blocked with 1% bovine serum albumin  and incubated overnight at 4 °C with primary antibodies , goat polyclonal P2X1,5,7 . Cells were stained with secondary antibody goat anti-rabbit  or rabbit anti-goat  AF488. Nuclear staining was performed with Vectashield Antifade containing DAPI . Cell imaging was performed using laser-scanning confocal microscope Zeiss LSM510 META . Cells at 2.5 × 106/mL) were incubated for 10 min at room temperature (RT) with Fc block  and immunostained with anti-human P2X4  or hIgG isotype control  followed by AlexaFluor 488 (AF488) . Cells were acquired on the Cytoflex instrument . Analyses were performed on CytExpert software . Flow cytometry—Cells . Concentration-response curves were fitted assuming a Hill coefficient of 1. Hypothesis testing for experiments with paired datasets were performed by means of paired Student’s


Ca2+ entry into the mitochondria is mediated by a highly selective Ca2+ channel called the mitochondrial calcium uniporter, which consists of a pore-forming subunit MCU and regulatory subunits such as MICU1. Although pharmacological regulation of the mitochondrial Ca2+ influx is a promising approach to controlling the cellular functions, a cell-permeable and specific inhibitor of the mitochondrial calcium uniporter has not yet been developed. Here, we identify a novel cell-permeable inhibitor of the uniporter by a high-throughput screening of 120 000 small-molecule compounds. In our study, DS16570511 dose-dependently inhibited serum-induced mitochondrial Ca2+ influx in HEK293A cells with an IC50 of 7 μM. DS16570511 inhibited Ca2+ uptake of isolated mitochondria from human cells, rat heart and pig heart. Overexpression of hMCU or hMICU1 in HEK293A cells increased mitochondrial Ca2+ influx, and the increases were completely suppressed by the pretreatment with DS16570511. DS16570511 also blocks mitochondrial Ca2+ overload in a Langendorff perfused beating rat heart. Interestingly, DS16570511 increased cardiac contractility without affecting heart rate in the perfused heart. These results show that DS16570511 is a novel cell-permeable inhibitor of the mitochondrial calcium uniporter and applicable for control of the cardiac functions.In cardiac myocytes, regulation of mitochondrial Ca The Ca2+ influx into the mitochondria is mediated by an inward-rectifying, highly selective Ca2+ channel called the mitochondria calcium uniporter. Although molecular components of the uniporter have been completely unknown for a long time, recent studies have revealed that the Mcu gene encodes a channel-forming unit of the uniporter. MCU is a mitochondrial inner membrane protein, and the multimer forms a Ca2+ channel in the lipid bilayer.6 In addition, the uniporter activity is positively or negatively tuned by some regulatory subunits such as MICU1.10 MICU1 is a calcium-binding EF hand-containing protein that regulates the uniporter activity in a Ca2+ concentration-dependent manner. MICU1 functions as a gatekeeper of the uniporter by inhibiting MCU activity at resting Ca2+ levels, whereas it has a stimulatory role in agonist-challenged cultured cells.11 At present, the regulatory mechanism of the uniporter activity is still under discussion.Mitochondria store large amounts of Ca2+ regulation.15 For example, in isolated cardiomyocytes, siRNA-mediated knockdown of Mcu enhances peak levels in cytosolic Ca2+ oscillation, which results in an increase of muscle contraction.16 In pathological aspects such as acute ischemia-reperfusion, cellular injury caused by mitochondrial Ca2+ overload is protected by cardiomyocyte-specific knockout of the Mcu gene in mice.17 Importantly, recent human genetic study has shown that mutation of MICU1 causes brain and muscle disorders.18 This study showed that fibroblasts of patients exhibited increased mitochondrial Ca2+ influx. Therefore, excess Ca2+ influx into mitochondria is toxic for cells and tissues in both acute and chronic pathological situations.The identification of the genetic component of the mitochondrial uniporter has provided new opportunities to understanding the biological roles of mitochondrial Ca2+ channel activity mediated by recombinant MCU multimers in the lipid bilayer is inhibited by RuR, showing that RuR directly acts on MCU to inhibit the channel activities.6 Although RuR potently inhibits the uniporter activities, its pharmacological use is limited owing to two factors: (1) the impermeability of the plasma membrane and (2) its unspecific inhibitory action against various ion channels.6 Therefore, cell-permeability and identifying the specific inhibitor of the uniporter are very important in pharmacology; although, no specific chemical uniporter inhibitor has yet been reported.The physiological and pathological evidences suggest that inhibition of the mitochondrial calcium uniporter activity is a novel and unique approach to controlling the cellular functions or to treating mitochondrial diseases. The most well-known inhibitors of the mitochondrial calcium uniporter are the polycationic compounds, Ruthenium Red (RuR) and Ruthenium 360 (Ru360). The Ca2+ influx in HEK293A cells. We found that DS16570511 blocked the Ca2+ influx in the cultured cells and isolated mitochondria. DS16570511 blocked the MCU- or MICU1-dependent increases of Ca2+ influx. Isolated perfused heart experiments revealed that the novel inhibitor has an inotropic effect in addition to the protective effect against mitochondrial Ca2+ overload.In the present study, we performed a high-throughput screening for small-molecule inhibitors of the mitochondrial Ca2+ indicator protein aequorin were established to detect dynamic mitochondrial Ca2+ influx. By using the cell-based assay as the first screening system, we screened 120 000 small-molecule compounds. The screening flow of this exploratory project is depicted in 50 of the hits was to be below 10 μM in the cell-based mitochondrial Ca2+ influx assay. (2) IC50 in the mitochondrial assay was to be at least sevenfold smaller than the IC50 in a counter assay using cytosol-targeted aequorin . (3) The inhibitory activity was also to be observed in isolated mitochondria from the human cells. (4) The inhibition was also to be observed in mitochondria prepared from small and large animals (in this case rats and pigs). (5) The inhibitory activity was to be reproducibly observed in the resynthesized compound. We found that DS16570511 was the only one of the hit compounds meeting all of these criteria . HEK293A cell lines expressing the mitochondria-targeted Cacriteria  and S3.2+ level  direct blockade of the calcium uniporter and (2) disruption of the membrane potential.21 Mitochondrial Ca2+ uptake is mediated by electrical potential across the mitochondrial inner membrane, which is generated by the oxidative phosphorylation system. Consistent with the previous studies, we observed that carbonyl cyanide-p-trifluoromethoxyphenylhydrazone (FCCP), an uncoupler of the mitochondrial membrane potential, significantly inhibited the Ca2+ uptake activity of isolated pig heart mitochondria ; pIRES-puro vector from Clontech Laboratories, Inc. ; HEK293A cells, pcDNA3.1, Hanks Balanced Salt Solution, coelenterazine h and a mitochondria isolation kit from Thermo Fisher Scientific Inc. ; fetal bovine serum and RuR from Sigma-Aldrich, Inc. ; JC-10 from Enzo Life Sciences, Inc. .Nine-week-old male Wistar rats were purchased from Japan SLC . All experimental procedures were performed in accordance with the in-house guidelines of the Institutional Animal Care and Use Committee of Daiichi Sankyo. The animals received a standard laboratory diet and filtered water 2+ levels in intact cells, HEK293A cells were stably transfected with pIRES-puro vector expressing mitochondria-targeted aequorin.20 One day after plating on a 15-cm dish (8×106 cells/dish), the cells were harvested and incubated with 2.5 μM coelenterazine h in aequorin assay buffer  and 0.1% bovine serum albumin) for 2 h at room temperature. The cells (8.1×104 cells/well) were then treated with DS16570511 for 20 min in a 96-well plate at room temperature. For induction of intracellular Ca2+, the cells were treated with 10% fetal bovine serum, and luminescence levels were measured by using a Centro LB960 luminometer .For dynamic measurements of mitochondrial Ca2+ uptake activity by MCU or MICU1, HEK293A cells were transiently transfected with the mitochondria-targeted aequorin vector and pcDNA3.1 vector containing human Mcu (NCBI Reference Sequence: NM_138357.2) or Micu1 (NM_001195518.1). One day after the transfection, the luminescence levels were measured as described above. MCU- or MICU1-dependent activity was calculated by subtracting the luminescence level of cells transfected with empty vector from that of cells expressing MCU or MICU1, respectively.In order to evaluate mitochondrial Ca2+ uptake activity of isolated mitochondria, mitochondria were prepared from the cells (1×108 cells) stably expressing mitochondria-targeted aequorin by using a mitochondria isolation kit. The mitochondrial pellet was suspended in 12 ml of swelling buffer ) containing 2.5 μM coelenterazine h. Five minutes after incubation of the mitochondria solution with DS16570511 in a 96-well plate at room temperature, CaCl2  was applied to the solution. The luminescence levels were detected as described above.To monitor Ca2  to the solution, the mitochondria were collected by centrifugation (3000 g) at 4 °C. The pellets were resuspended in the swelling buffer containing 1 μM RuR. After collection of the mitochondria by centrifugation (3000 g), the pellets were dried and dissolved by 40 μl of sulfuric acid at 95 °C. The solution was then diluted by water, and Ca2+ concentration of the solution was measured by atomic absorbance spectrometer .Mitochondria were isolated by using the mitochondria isolation kit and then dissolved in the swelling buffer. The protein concentration of the mitochondrial solution was 5 mg/ml. Thirty minutes after application of CaClμM JC-10. The protein concentration of the solution was 5 mg/ml. Five minutes after incubation of the mitochondria with DS16570511 at room temperature, fluorescence intensities were measured by a FlexStation 3  using the green channel (excitation/emission wavelength: 485/538 nm) or the red channel (excitation/emission wavelength: 485/612 nm).Pig heart mitochondria were prepared as described above and suspended in the swelling buffer containing 1 2, 1.2 mM KH2PO4, 1.2 mM MgSO4, 25 mM NaHCO3 and 5.5 mM glucose (pH 7.4)) equilibrated with 95% O2/5% CO2. A balloon was inserted through the left atrium into the left ventricle. Heart rate and contractility were continuously recorded with a data acquisition system .The hearts were rapidly excised from 9-week-old male Wistar rats and perfused at 37 °C with Krebs-Ringer perfusion buffer (127.2 mM NaCl, 4.7 mM KCl, 2.5 mM CaCl


Of these four metal cations, Ca2+ has the strongest stabilizing effect, followed by K+, Mg2+, and Na+ in a decreasing order. The binding of K+ to G-triplexes is accompanied by exothermic heats, and the binding of Ca2+ with G-triplexes is characterized by endothermic heats. G-triplexes formed from two G-triad layers are not stable at physiological temperatures; however, G-triplexes formed from three G-triads exhibit melting temperatures higher than 37°C, especially under the molecular crowding conditions and in the presence of K+ or Ca2+. These observations imply that stable G-triplexes may be formed under physiological conditions.G-triplexes are non-canonical DNA structures formed by G-rich sequences with three G-tracts. Putative G-triplex-forming sequences are expected to be more prevalent than putative G-quadruplex-forming sequences. However, the research on G-triplexes is rare. In this work, the effects of molecular crowding and several physiologically important metal ions on the formation and stability of G-triplexes were examined using a combination of circular dichroism, thermodynamics, optical tweezers and calorimetry techniques. We determined that molecular crowding conditions and cations, such as Na However, some sequences can form non-canonical DNA structures, including G-quadruplex+ and Na+ ions. A G-rich sequence with only three G-tracts may form a G-triplex with stacked G-triads, in which a central guanine connects with two other guanines by Hoogsteen-like hydrogen-bonds (789111291314151671819202122Intramolecular G-quadruplexes can form from a G-rich sequence containing four regions of two or more adjacent guanines (G-tracts), and these structures are stabilized by the monovalent Ken-bonds 78910. Th+ and Na+) and bivalent (Mg2+ and Ca2+) cations, particularly Ca2+, promote the formation of G-triplexes. This finding is different from that for G-quadruplexes, whose stabilities are almost unaffected by these two bivalent cations.Considering that the factors affecting the formation of G-triplexes have never been systematically studied, in this work, the effects of molecular crowding and several physiologically important metal ions on the formation and stability of G-triplexes were examined. We found that molecular crowding promotes the formation of G-triplex structures. We demonstrated that monovalent (K2T2T3) with four G-tracts have the potential to form G-quadruplexes. The 3'-most G-tract of each of these oligomers was truncated to generate the TBA11, Hum15 and T2T2 fragments, which potentially form G-triplexes. As G-triplet and G-quartet share similar stacking and loop geometry, it is reasonable to assume that circular dichroism (CD) signals reflecting the strand orientation in the G-quadruplex may also apply to the G-triplex. We therefore used CD spectroscopy to examine the secondary structures of these six oligonucleotides in the presence of different metal ions, as well as under dilute and molecular crowding conditions. CD spectroscopy was first conducted under dilute conditions.Six G-rich oligonucleotides were used to investigate the factors that influence G-triplex formation . Three o+ or Na+ had a positive peak at approximately 292 nm and a negative peak at approximately 269 nm   . The obs2T2 might be intermolecular G-quadruplexes rather than G-triplexes. It has been reported that the stability will increase with DNA concentration for intermolecular structures but not for intramolecular structures30Tm) of the DNA secondary structures. Similar to G-quadruplexes2T2 exhibited decreased UV absorption at 295 nm with increasing temperature  were determined with different DNA concentrations (7–15 μM). The results indicate that the Tm values were independent of DNA concentration in the presence of individual metal ions and under both diluted and crowded buffers . This result supports that the DNA secondary structures formed by Hum15 and T2T2, especially in the presence of Na+, Mg2+ or Ca2+, were intramolecular G-triplexes and not intermolecular G-quadruplexes. Melting temperature analysis indicated that the intermolecular G-quadruplexes formed by Hum9 were less stable than the proposed G-triplexes formed by Hum15 in the presence of K+, thus suggesting that Hum15 may preferentially form more stable intramolecular G-triplexes in the presence of K+.The G-rich Hum9 oligonucleotide , a truncout ions , indicat+, K+, Ca2+ and Mg2+ on the formation of G-triplexes under both dilute and molecular crowding conditions, the effects of these ions on G-triplex stability were compared , the G-triplex consisting of two G-triads (TBA11) was much less stable (Tm of 34.3°C in the presence of Ca2+ and under the molecular crowding condition). This observation may indicate that G-triplexes with two G-triads might not be stable at physiological temperatures. In contrast, the G-triplexes with three G-triads are much more stable, especially in the presence of K+ or Ca2+. Under the molecular crowding conditions, the Tm values of Ca2+-stabilized G-triplexes (Hum15 and T2T2) were 52.0°C and 64.5°C, respectively. With K+ or Ca2+ stabilization, G-triplex structures with three G-triads might be stable at physiological temperatures.Although the G-quadruplex consisting of two G-tetrads (TBA) was relatively stable in the presence of KTm provides a rough measurement for the stability of a structure at a specific temperature. To quantify thermodynamic stabilities, we used a two-state transition model to evaluate the free energy change at 37°C (ΔGθ37) of these G-rich sequences by analyzing the melting curves  of the interaction between two or more molecules in solution. This technique has been used to study the binding constant between G-quadruplexes and metal ions or ligands34+ and Ca2+ with G-quadruplexes or G-triplexes were also investigated using ITC assays. As shown in + to Hum21 and Hum15 is accompanied by exothermic heats. K+ binds the G-triplex Hum15 with a Ka value of 735 ± 65 M−1, which is ~17-fold weaker than that of K+ and the G-quadruplex Hum21 (Ka = (1.24 ± 0.12) × 104 M−1). This is consistent with the above observation that K+ has higher stabilizing ability to G-quadruplexes than to G-triplexes. The binding of Ca2+ with G-quadruplexes or G-triplexes is characterized by endothermic heats. In contrast to K+, Ca2+ shows a higher binding affinity to G-triplex (Ka = (2.94 ± 0.41) × 104 M−1) than to G-quadruplex (Ka = (8.86 ± 0.92) × 103 M−1). And as expected, Ca2+ shows a higher binding affinity to G-triplex than K+. These results are also consistent those of CD and melting assays, thus further demonstrating that Ca2+ is a better G-triplex stabilizer than K+.Isothermal titration calorimetry (ITC) is a quantitative technique that can determine the binding affinity . When single-stranded Hum15 tethered between two duplex handles was stretched, a characteristic unfolding event was observed from the force-extension curve, indicating that folded structure was formed in the Hum15  of 5.1 ± 0.5 nm. This ΔL value matches very well with that of G-triplex+ to 48 ± 3% under 100 mM Ca2+, indicating that the capability of Ca2+ to promote G-triplex formation is better than that of K+. The G-triplex-promoting ability of Ca2+ could also be observed when Ca2+ has a low concentration. For example, in the presence of 100 mM K+ + 2 mM Ca2+, 32 ± 2% Hum15 could fold into G-triplexes.Optical tweezers represent a unique approach to investigate the mechanical stability of folded nucleic acid structures at the single-molecule level14383940he Hum15 . In the +, and from 32 ± 2% to 35 ± 3% in the presence of 100 mM K+ + 2 mM Ca2+.Since stable optical trapping requires a significant difference in the refractive index of a trapped particle and solvent, we used 40% dimethyl sulfoxide (DMSO) to simulate molecular crowding conditionsInterestingly, the rupture forces under these conditions are similarly located at 31–35 pN . These v+, K+, Ca2+ and Mg2+, promote and stabilize G-triplex formation. The strength of the G-triplex-stabilizing effects of these ions is found to be: Ca2+>K+>Mg2+>Na+. Ca2+ promotes the formation of parallel G-triplexes under either dilute or molecular crowding conditions. G-triplexes formed in the presence of K+ often have strand orientations similar to those of G-quadruplexes formed in the corresponding oligonucleotides containing four G-tracts. The melting temperatures of the G-triplexes consisting of two G-triads are lower than physiological temperature, even under the molecular crowding condition and in the presence of Ca2+. However, the G-triplexes containing three G-triads have much higher melting temperatures, especially under the molecular crowding condition and in the presence of K+ or Ca2+, implying that stable G-triplexes may be formed at physiological temperatures. The binding of K+ to G-triplexes is accompanied by exothermic heats, and the binding of Ca2+ with G-triplexes is characterized by endothermic heats. The G-triplex-promoting and stabilizing effect of Ca2+ is noteworthy, as Ca2+ is a physiologically important metal ion and it exhibits rather weak or undetectable effect on the G-quadruplex structures. As G-quadruplexes are believed to have important biological function and the number of putative G-triplex-forming sequences might be more prevalent in the human genome than putative G-quadruplex-forming sequences, our study may pave a way for further studies on the G-triplexes, such as whether G-triplexes form in vivo and whether they have biological functions.In summary, we demonstrated that G-triplex structures can be formed by G-rich sequences containing only three G-tracts. In addition, molecular crowding and the presence of physiological important metal ions, such as Nahttp://www.idtdna.com/analyzer/Applications/OligoAnalyzer). Na2EDTA (Disodium ethylenediamine tetraacetic acid), Tris [tris(hydroxymethyl)aminomethane], KCl, NaCl, MgCl2, CaCl2, PEG 200, HCl were obtained from Sigma. Deionized and sterilized water (resistance > 18 MΩ/cm) was used throughout the experiments.The oligonucleotides listed in 2EDTA, and 100 mM individual metal ions. Under molecular crowding conditions, 3 mL reaction mixture was prepared in 10 mM Tris–HCl buffer (pH = 7.0) containing 2.5 μM individual DNA oligonucleotides, 0.5 mM Na2EDTA, 400 mL/L PEG 200, and 100 mM individual metal ions. The mixture was heated at 95°C for 5 min, cooled to 25°C and then incubated at 4°C overnight. CD spectrum of the mixture was recorded between 230 and 320 nm in 1-mm path length cuvettes on a Jasco J-715 spectropolarimeter. Spectra were averaged from three scans, which were recorded at 100 nm/min with a response time of 1 s and a bandwith of 1.0 nm.Under dilute condition, 3 mL reaction mixture was prepared in 10 mM Tris–HCl buffer (pH = 7.0) containing 2.5 μM individual DNA oligonucleotides, 0.5 mM Na2EDTA, 100 mM Na+ or 100 mM K+ or 100 mM Mg2+ or 100 mM Ca2+. Under molecular crowding conditions, additional 400 mL/L PEG 200 was added. The solution was heated to 95°C for 5 min, then cooled rapidly to 25°C and was allowed to incubate at 25°C for 30 min and overnight incubation at 4°C. After a sufficient mixing, the absorption signal at 295 nm (400 nm as control wavelength) was recorded at about 10°C. When the absorption signal became constant, the temperature was increased in steps of 1°C. At each temperature, the mixture was left to equilibrate for at least 1 min. Absorption signal was recorded when the signal did not change any more.Melting temperature detection of G-quadruplexes or G-triplexes was carried out on a Cary-60 UV-vis spectrophotometer equipped with a single cell peltier temperature control accessory. Under dilute condition, the G-quadruplexes (10 μM) or G-triplexes (10 μM) solution were prepared in 10 mM Tris–HCl buffer (pH = 7.0) containing 0.5 mM Na2+ (1 mM) solutions were both prepared in 10 mM Tris–HCl buffer (pH = 7.0). K+ (1 mM) solution was prepared in 10 mM Tris–HCl buffer (pH = 7.0) containing 0.5 mM Na2EDTA. All of the solutions were heated to 95°C for 5 min, then cooled rapidly to 25°C and were allowed to incubate at 25°C for 30 min and overnight incubation at 4°C. Then the ion  solution was titrated into the corresponding DNA (50 μM) solution. The titration included an initial injection of 0.4 μL ion solution followed by 19 injections of 2 μL ion solution every 120 s with stirring at 750 rpm at 16°C. To define the baseline, the ion was titrated into the same buffer without DNA under the same conditions. The titration data and binding plots after the baseline were subtracted were analyzed using MicroCal Origin software with the one-site binding model.Isothermal titration calorimetry (ITC) measurements were performed using a MicroCalTM isothermal titration calorimeter iTC200 . DNA (50 μM) and CaL) at a particular force (F) was calculated as the extension difference between the stretching and the relaxing traces at that forceThe DNA construct for single-molecule assay was prepared by sandwiching Hum15  between Supporting information


SR)‐derived Ca2+ release is required to induce Ca2+ oscillations for contraction and to control cardiac development through Ca2+‐activated pathways. Here, our aim was to study how SR Ca2+ release regulates cytosolic and nuclear Ca2+ distribution and the subsequent effects on the Ca2+‐dependent localization of class IIa histone deacetylases (HDAC) and cardiac‐specific gene expression in embryonic cardiomyocytes. Confocal microscopy was used to study changes in Ca2+‐distribution and localization of immunolabeled HDAC4 and HDAC5 upon changes in SR Ca2+ release in mouse embryonic cardiomyocytes. Dynamics of translocation were also observed with a confocal microscope, using HDAC5‐green fluorescent protein transfected myocytes. Expression of class IIa HDACs in differentiating myocytes and changes in cardiac‐specific gene expression were studied using real‐time quantitative PCR. Inhibition of SR Ca2+ release caused a significant decrease in intranuclear Ca2+ concentration, a rapid nuclear import of HDAC5 and subnuclear redistribution of HDAC4. Endogenous localization of HDAC5 and HDAC4 was mostly cytosolic and at the nuclear periphery, respectively. Downregulated expression of cardiac‐specific genes was also observed upon SR Ca2+ release inhibition. Electrical stimulation of sarcolemmal Ca2+ influx was not sufficient to rescue either the HDAC localization or the gene expression changes. SR Ca2+ release controls subcellular Ca2+ distribution and regulates localization of HDAC4 and HDAC5 in embryonic cardiomyocytes. Changes in SR Ca2+ release also caused changes in expression of the developmental phase‐specific genes, which may be due to the changes in HDAC‐localization.In embryonic cardiomyocytes, sarcoplasmic reticulum ( Secondly, we aimed to study how changes in Ca2+ distribution affect the Ca2+‐dependent nuclear signaling that can lead to changes in class IIa HDAC localization and cardiac‐specific gene expression in embryonic cardiomyocytes.This study aimed to determine if SR Ca2+ mobilization and immunolabeling experiments performed at the University of Eastern Finland, using the C57BL/6JOlaHsd strain. In these experiments, where two different mouse strains were used, the results were comparable between the mouse lines. All experimental designs were approved by the Animal Use and Care Committee of the University of Oulu and the National Animal Experiment Board in Finland.The method for cell isolation and culturing of the E9‐11 embryonic cardiomyocytes has been described previously  on an imaging plane where all regions of interest  were clearly visible. The cells were perfused with culture medium containing a pharmacological inhibitor and simultaneously stimulated electrically  and line‐scanned periodically to observe changes in subcellular Ca2+ signals. Pharmacological interventions used were thapsigargin , ryanodine  and Ca2+ ionophore, A23187 . As we were interested on the acute effect of these pharmacological interventions, the cells were imaged with the treatment until Ca2+ oscillations stopped or changes in Ca2+ distribution could be clearly observed (≤20 min). The line scan images were analyzed with the FluoView 1000  and Origin 2016  programs. Fluo‐4 fluorescence intensity is expressed as an F/F0 ‐ratio, where F is the background subtracted fluorescence intensity and F0 is background subtracted minimum fluorescence value measured from each region at rest at control conditions. Fluorescent intensity in the three different intracellular locations was normalized to its own F0 value. Changes in Ca2+ signals were compared within each subcellular localization (control vs. treatment). No comparison between the localizations was conducted as this would have required calibration of the fluorescent dye at each subcellular localization.Caμmol/L A23187, 50 μmol/L ryanodine or 10 μmol/L thapsigargin) and then immediately fixed and labeled, using a method described previously  with or without pharmacological intervention . Here, after background subtraction, fluorescence measured in representative cytosolic and nuclear regions was used to calculate the n/c ratio. HDAC4 on the other hand was mainly nuclear, therefore the fluorescent signal was measured from the nuclear periphery and the center of the nucleus. These values were then normalized to the background subtracted cytosolic fluorescence.μL Opti‐MEM (Invitrogen) containing 0.2 μg HDAC5‐GFP‐plasmid and 2 μL Lipofectamine 2000 (Invitrogen). After a 5‐h incubation at 37°C, the medium was changed to Dulbecco's Modified Eagle Medium (DMEM) containing 10% fetal bovine serum  and 1% penicillin‐streptomycin  and the cells were cultured for additional 18–24 h to allow expression of the chimeric protein.After culturing for 18–24 h, E9‐11 cardiomyocytes were transfected with a plasmid containing HDAC5‐GFP , using Lipofectamine 2000 (Invitrogen) according to the manufacturer's instructions. Briefly, cells cultured on glass bottom culturing dishes were transfected with 100 2/5% CO2). Electrical stimulation (0.5 Hz) was maintained throughout the experiments with two platinum electrodes placed on opposing sides of the plate. The change in cytosolic versus nuclear fluorescence with pharmacological interventions (50 μmol/L ryanodine or 10 μmol/L thapsigargin) was followed over time and results expressed similarly to the immunolabeled HDAC5, as a n/c – ratio.A confocal inverted microscope  was used to take images of the HDAC5‐GFP with excitation at 488 nm and emission collected at 500–600 nm through a 60x objective lens. A steady temperature of 34°C was maintained by continuous superfusion with preheated culturing medium . When comparing the effect of SR Ca2+ release inhibition to cardiac‐specific gene expression, the results were further normalized to the control in each group. The primers and fluorescent probes are listed in Table Tissue samples (ventricles) from mouse embryos at different developmental stages  were used in these experiments. RNA extraction, cDNA synthesis and RT‐qPCR were performed as previously described  caused the SR‐derived Ca2+ oscillations to stop, similarly as has been shown by previous studies  from the SR. Here, stimulation was possible for a short amount of time and Ca2+ oscillations stopped as soon as the SR was depleted.In spontaneously active embryonic cardiomyocytes strong Casigargin 0 μmol/L 2+ levels both in the cytosolic region near the SR and within the nucleus  or thapsigargin (10 μmol/L), resulted in strong HDAC5 nuclear import and subnuclear redistribution of HDAC4 toward the nuclear periphery , to enable strong Ca2+ influx. This caused a rapid increase in [Ca2+] throughout the cell, with the most pronounced increase observed within the nucleus  and depletion of the SR Ca2+ stores  both caused a strong and relatively rapid nuclear accumulation of HDAC5‐GFP  and Calr were observed. Casq expression was downregulated, whereas Calr was upregulated upon SR Ca2+ release inhibition. Due to the developmental differences in Calr and Casq expression, where the latter takes over the role as the major Ca2+ buffering protein as the muscle matures . Significant changes in HDAC4 and HDAC5 intracellular distribution and cardiac‐specific gene expression were observed when SR Ca2+ release was inhibited.In this study, we show that in embryonic cardiomyocytes SR Ca2+ signaling has an essential role in the development of the early cardiac tissue and the first asynchronous Ca2+ oscillations can be observed already at the early stages of cardiac crescent development. At this stage, no sarcomeric banding or myocyte contraction can be observed , we observed strong changes in Ca2+ distribution with the diminishing Ca2+ oscillations.In embryonic cardiomyocytes, Ca2+ release‐mediated changes in [Ca2+]nuc can regulate expression of the cardiac‐specific TFs Nkx2.5, GATA4 and MEF2C  showed that HDAC5 can facilitate stabilization and nuclear accumulation of the hypoxia‐inducible factor 1α (HIF‐1α). This action was accomplished by cytosolic deacetylation of the heat shock protein 70 (Hsp70) and subsequent stimulation of a pathway that led to nuclear accumulation of HIF‐1α. This is a very interesting finding because hypoxia has been found to stimulate multiple different developmental pathways in the developing embryo (Chen et al. During the early developmental stages, strict temporal regulation of developmental‐specific gene expression is necessary for the tissue development to ensue in a correct manner. Therefore, a dynamic regulation of class IIa HDAC localization is to be expected, as the genes need to be repressed fast if, for example, activity of the cell changes. Our results do indeed show this, as uniform intracellular distribution of HDAC5 can be changed to mostly nuclear rapidly, due to the ability of differentiating myocytes to rapidly modify their [Ca2+ release can initiate spontaneous cardiomyocyte contraction and create strong Ca2+ gradients across the cell (Korhonen et al. 2+ homeostasis can have a significant effect on [Ca2+]nuc, and thereby on various Ca2+‐activated pathways, some of which can control the subcellular localization of HDAC4 and HDAC5. Furthermore, SR Ca2+ release was shown to have a significant role in regulation of cardiac‐specific gene expression. These results provide a potential link between SR Ca2+ release and cardiac development, thereby also shedding light on the embryonic lethal knock‐out models that disrupt SR Ca2+ release.In the embryonic cardiomyocytes, SR CaNone declared.


Using high-resolution microscopy, we have identified a subset of docked insulin granules in human β cells and rat-derived clonal insulin 1 (INS1) cells for which localized Ca2+ influx triggers exocytosis with high probability and minimal latency. This immediately releasable pool (IRP) of granules, identified both structurally and functionally, was absent in β cells from human T2D donors and in INS1 cells cultured in fatty acids that mimic the diabetic state. Upon arrival at the plasma membrane, IRP granules slowly associated with 15 to 20 L-type channels. We determined that recruitment depended on a direct interaction with the synaptic protein Munc13, because expression of the II–III loop of the channel, the C2 domain of Munc13-1, or of Munc13-1 with a mutated C2 domain all disrupted L-type channel clustering at granules and ablated fast exocytosis. Thus, rapid insulin secretion requires Munc13-mediated recruitment of L-type Ca2+ channels in close proximity to insulin granules. Loss of this organization underlies disturbed insulin secretion kinetics in T2D.Loss of first-phase insulin secretion is an early sign of developing type 2 diabetes (T2D). Ca Theseux sites , 8, 9. A opening –12. Indechannels , 14.2+ entry into human and rodent β cells occurs via L- and P/Q-type Ca2+ channels (reviewed in ref. C-type channel (CaV1.2) (D (CaV1.3) (2+ channels (C channel (CaV1.2) selectively ablates fast exocytosis in mouse β cells  , 16, whi(CaV1.3) –19. In h(CaV1.3) , 20. L- (CaV1.3) , 22, whi(CaV1.3) –27. The channels . A simil β cells , 29–31. synapses , 22. Altsynapses , that cosynapses  or help 2+ channels in human β cells and the insulin-secreting cell line, INS-1. We show that L-type Ca2+ channels are recruited to a subset of the docked granules, probably by direct interaction with Munc13 at the release site. Functionally, this places microdomains of tens of μM Ca2+ near certain granules, resulting in a rapid exocytosis that is synchronized with the depolarization, while global Ca2+ is less important. Intriguingly, this organization is absent in β cells from human T2D donors, suggesting a molecular mechanism for the early loss of first-phase secretion in the disease.Here, we used high-resolution live-cell imaging to directly assess the spatial relation between granules and Ca2+] and exocytosis in β cells from nondiabetic (ND) human donors (https://doi.org/10.1172/JCI88491DS1 for details) using total internal reflection fluorescence (TIRF) microscopy (2+ indicator Fluo5F (DK ~2.3 μM) and the slow Ca2+ chelator EGTA (both supplied as acetomethoxy esters). The latter narrows the Fluo5F/Ca2+ signal from individual Ca2+ influx sites by restricting Ca2+ diffusion (+ (75 mM for 1 s every 10 s) from a pressurized glass pipette. Relatively high K+, together with the ATP-sensitive potassium channel (KATP channel) opener diazoxide, essentially clamps the membrane potential, resulting in steep depolarizations and rapid opening of voltage-gated Ca2+ channels. During K+ pulses, the Fluo5F signal increased by about 4-fold and returned toward baseline in the interval between . Exocytosis was significantly faster during the short depolarizations than during the interval between   .2+ influx, we compared the Fluo5F signal at granules that responded to the depolarization with exocytosis (responders) with that at granules that remained docked during the experiment (failures). The individual Fluo5F image sequences suggested local influx of Ca2+ near responders (0), was higher at responder granules than at failures   with those between pulses . Thus, depolarization-induced [Ca2+] entry occurs preferentially near granules that are released with short latency and high probability. We reached similar conclusions with human β cells stimulated with elevated glucose  . The dif glucose  or tolbu glucose .+   (P = 0.0001 by logistic modeling). Notably, Ca2+ influx in T2D β cells was not localized to granules , even in the few cells that had normal cell-averaged Ca2+ amplitudes . When these β cells were depolarized with K+ , the Ca2 = 0.05) . Inspectodeling) . Howevergranules , Avg T2D2+ influx into INS1-832/13 cells, a widely used insulin-secreting rodent cell line that shares many features with primary β cells and is easy to transfect (2+] with a membrane-targeted version of R-GECO (lyn-R-GECO) in EGTA-loaded cells. As in the human cells, short, repeated pulses of elevated K+ (1 s every 5 s) or trains of voltage-clamp depolarizations resulted in pulsatile increases in submembrane [Ca2+]   . In cells loaded with the faster Ca2+ chelator BAPTA , [Ca2+] still rose faster at responders than at failures but was slowed in both groups relative to EGTA-loaded cells  . Spatiotfailures , which c2 cells) . For graed cells . When thgranules . Likewisgranules , prevent+ influx . In both2+ influx through clusters of 3 to 20 Ca2+ channels with realistic opening kinetics in space and time (see Methods), assuming either added cytosolic EGTA (1 mM) or no exogenous Ca2+ buffer. This analytical approach indicated that microdomains with time-averaged [Ca2+] of greater than 5 μM and a radius similar to that of a granule (100 nm) form around Ca2+ channels . Further, it allowed us to calculate the exocytosis rate (cumulative hazard) as a function of the distance to Ca2+ channels , and confirmed that it traffics correctly to the plasma membrane  in INS1 cells . EGFP-Ca imaging . Single  imaging , were mo imaging . We obta imaging  and calc–14 m2/s . Visuall–14 m2/s . On aver–14 m2/s . Superre–14 m2/s  indicate2+ channels affects exocytosis, we expressed EGFP-CaV1.2 and NPY-mCherry in human β cells and depolarized them with elevated K+ for 40 seconds. As expected, exocytosis in cells from T2D donors was only one-third of that in cells from ND donors , with the strongest reduction occurring during the initial burst    tended to have more associated EGFP-CaV1.2 than did later responders . In the K+ group, a ΔF/S increase of 0.1 augmented the rate of exocytosis by approximately 20% . In contrast, for the other 2 groups, there was no statistical evidence of an effect of the ΔF/S signal on the exocytosis rate  , and 50%granules . As in h 0.0003) . Consistsponders , and grasponders . When stsis rate . Followisis rate , similarsis rate .2+ channel association with granules. Consistent with a role of Munc13 in granule priming, EGFP-Munc13 localized to responder granules but not to failures in human ND cells    . Both Mulls, NS) . The dat6 cu/(W × s) when the exposure time (50 ms) and excitation power (1 mW) are considered. By dividing this value with the fluorescence of a single EGFP molecule (0.82 ± 0.01 × 106 cu/(W × s); The fluorescence intensities of EGFP-CaV1.2 clusters were used to estimate how many channels are present within a granule-associated cluster. The average ΔF value in the experiments depicted in 2+ channels and Munc13. Upon depolarization, these granules are exposed to microdomains of high [Ca2+], which strongly increases their release probability and decreases their latency. As a consequence, exocytosis and insulin release are efficiently coupled to cellular electrical activity rather than the bulk cytosolic [Ca2+] that accumulates as a consequence of channel opening. The rapid-release kinetics and number of these granules suggest that they are identical with the immediately releasable pool (IRP) that has been defined electrophysiologically in β cells  or by randomizing the location of the Ca2+ source (ACh causing release from stores). The effects of Ca2+ channel clustering on insulin secretion will be strongest during short depolarizations, and it should be pointed out that individual glucose-dependent action potentials last only about 50 ms and their bursts no longer than a few seconds. Because of this, the lack of Ca2+ channel association may also underlie the disturbed first-phase release in diabetic patients. Indeed, knockout of L-type channels in mouse β cells preferentially disrupts first-phase insulin secretion  in response to ACh will recruit Munc13 and related proteins such as Ca2+ dependent activator protein for secretion (CAPS) and double-C2 domain (Doc2) to the plasma membrane  and between pulses when Ca2+ channels were closed. This may be explained by the presence of a small pool of highly Ca2+-sensitive granules (HCSP) with an apparent DK that is at least 10-fold lower than that of IRP granules  contains approximately 400 docked granules, of which approximately 100 are associated with a Ca2+ channel cluster. On average, each of these granule-associated clusters contains 15–20 channels, or at least 1,500 in total. We acknowledge that this is higher than our earlier estimate of approximately 500 active channels in mouse β cells  and Munc13  were provided by H. Mulder  and maintained as described previously . For expThe constructs used were the granule marker NPY-mCherry  and the 2, 2.6 mM CaCl2, 3 mM D-glucose, and 5 mM HEPES (pH 7.4 with NaOH) at 32°C, or 25°C for single-molecule imaging. For exocytosis experiments, the same buffer contained 10 mM glucose, 200 μM diazoxide, and 2 μM forskolin. Solutions containing oleate or palmitate (0.5 mM) were prepared as described previously (+ (75 mM equimolarly replacing Na+) through a pressurized glass electrode. Cells were exposed for no longer than 40 seconds, during which the effects of elevated K+ on cellular metabolism are likely minimal . Excitation was from 2 diode-pumped solid-state (DPSS) lasers at 491 and 561 nm (Cobolt) passed through a cleanup filter  and controlled with an acousto-optical tunable filter (AA Opto Electronic). Excitation and emission light was separated using a beamsplitter . The emission light was chromatically separated onto separate areas of an electron-multiplying charge-coupled device (EMCCD) camera  using an image splitter , with a cutoff at 565 nm  and emission filters . Scaling was 160 nm per pixel. For still images, the red and green color channels were acquired sequentially, first with cells exposed to 491 nm (1 mW) for 1 second (50 × 20 ms average), immediately followed by 561 nm (0.5 mW) for 100 ms; bleed-through from mCherry into the green channel was 0.06% ± 0.01%. For movies, cells were excited simultaneously with 491 and 561 nm light and recorded in stream mode with 100-ms exposures (10 frames/s), a 1-s exposure  or in a circle of 0.5 μm (F) and divided by the prestimulation value (Fcell0 or F0). Immobile, docked granules were identified by eye. Colocalization of EGFP-labeled proteins with granules was measured as described previously (c) of 3 pixels (0.5-μm) in diameter; b) a surrounding annulus (a) with an outer diameter of 5 pixels (0.8-μm); and c) a background area not touching any cell (bg). The circle contains all of the fluorescence originating from the docking site; it also contains fluorescence from molecules not bound to the docking site, which is estimated using a. To obtain the specific on-granule fluorescence ΔF, the annulus value (a) was therefore subtracted from that of the circle (c) (ΔF = c – a). To obtain off-granule fluorescence, the annulus value was background corrected (S = a – bg). S represents the local unbound concentration of the labeled protein, and averaged for each cell, S is linearly related to the protein’s expression level. For many proteins, the relationship of ΔF to S follows a 1-site binding equation that reaches saturation at higher expression levels . Exocytosis, docking and visiting events were detected manually (1/2t) at each granule were obtained by fitting a Hill expression F = Fmaxtn/[(1/2t)n + tn] to the signal during the first K+ pulse. Single molecules were traced using the ImageJ plug-in Particle Tracker , and all Ca2+ channel clusters (3–20 channels per cluster) were equivalent and uniformly distributed over its surface. The low density of Ca2+ channels in β cells  of 20 μM and a thickness of 50 nm for the layer in which the buffer is present were assumed, corresponding to approximately 600 molecules/μm2. Kinetics, rate constants, and affinity for R-GECO were taken from the literature (2+ channel current is iCa = gCa (V – VCa), where the gCa of approximately 2 picosiemens is the single-channel conductance (VCa is approximately 65 mV (in 2.6 mM extracellular Ca2+). Depolarizing the cell with 75 mM K+ results in a membrane potential (V) of approximately 0 mV  software  (2+-bound GECO levels were post-processed by convolving with the point spread function (PSF) of the microscope and averaged over the acquisition time (100 ms). MATLAB (MathWorks) was used to simulate channel gating and to perform post-processing. The simulations of Ca2+ influx quantitatively support the conclusion that Ca2+ influx occurred near granules. However, the spatial Ca2+ gradients that develop at the channel pore are blurred by limitations of the indicator and the microscope. Instead, we used the rise time of the local Ca2+ signal to estimate distances of granules from the nearest channel cluster. Simulations showed that the rise time, in contrast to the signal amplitude, is nearly independent of the number of channels per cluster. Measured rise times are limited by the finite speed of the K+-mediated depolarization  describing diffusion and mutual binding of Ca β cells  allowed terature . The secterature . Finallyductance  and the ely 0 mV  66), gi, gi2+ iotor.org) . CalC us ~50 ms,  and the t test for 2-tailed, paired or unpaired samples, as appropriate. A P value of less than 0.05 was considered statistically significant. To test whether exocytosis was more frequent during defined time periods (K+ pulses) and whether there were differences between healthy and diabetic cells, we used χ2 tests (for INS1 cells) and a logistic regression model (for human cells), adjusting for the different durations of K+ pulses and intervals between pulses. We determined the rise time of the experimental R-GECO signal (1/2t) at each granule by fitting a Hill expression F = Fmaxtn/[(1/2t)n + tn] to the R-GECO signal during the first pulse, granule by granule. The ΔF/S signal for EGFP-CaV1.2 was calculated as the average over the 10 seconds before the stimulus. To quantify how 1/2t influences the rate of exocytosis, we fitted Cox’s proportional hazards regression models with, respectively, log(1/2t) or the ΔF/S signal as a covariate. We tested for evidence for a potential time-varying effect of the rise time on the rate of exocytosis in the data, but found that the data were well described by a time-constant effect of rise time or ΔF/S, respectively. To account for cell-to-cell variation, granules within a cell were considered clustered data, and a marginal Cox model was used to obtain valid estimates of standard errors  and the fitting for Data are presented as the mean ± SEM unless otherwise stated. Statistical significance was assessed using Students d errors . To relaHuman pancreatic islets were isolated and provided by the Nordic Network for Clinical Islet Transplantation  with full ethics board approval and informed consent (for donor information, see NRG, PY, and SB performed and analyzed imaging experiments. PY and PEL performed electrophysiology experiments. SB and NRG designed experiments. MR and MGP performed modeling and analyzed data, MGP conceived modeling, and VM and AS consulted the modeling. MVC and PR generated EGFP-CaV1.2. GC conceived and performed the statistical analyses. SB conceived the study and wrote the manuscript. All authors gave feedback and approved the final version of the manuscript.


Acute urticaria is the presence of urticaria for <6 weeks, and it is the most common type of urticaria in children. Sometimes, it may be associated with anaphylaxis, a life-threatening condition. Urticaria must be differentiated from anaphylaxis because the latter may require emergency treatment. We describe a child with anaphylaxis exposed to grasses on two occasions.Cynodon dactylis, Phalaris arundinacea and Festuca elatior. Little is known about the importance of pollens as a cause of urticaria in young children.We described a 4-year-old male child with anaphylaxis exposed to grasses. Patient also suffered mild neurologic/respiratory symptoms but it is unlikely that he had anaphylaxis. Skin-prick tests were positive to The case reported here is particularly interesting because, to the best of our knowledge, anaphylaxis due to pollen exposure in children aged <4 years has not been reported before. We strongly encourage all physicians searching for the cause of acute urticaria  to consider the possibility of pollen allergy, and to screen these patients for pollen sensitization. Urticaria is a heterogeneous skin disorder that can be acute or chronic, intermittent or persistent. It may occur alone or in association with other related conditions such as angioedema and, in its most severe form, as part of anaphylaxis . “Acute Allergic “triggers” are often considered in cases of acute urticaria. However, several other mechanisms by which acute urticaria may occur have been proposed. Infection may be the most common cause in pediatric cases. Hypersensitivity to drugs or food has been reported to be a common potential trigger. In general, urticaria is a self-limiting, innocuous condition but can present with systemic symptoms. It may be associated with anaphylaxis whereby the patient may experience gastrointestinal/respiratory symptoms, or angioedema .Anaphylaxis is a rapid-onset, severe, acute and potentially life-threatening condition. It is characterized by compromise of the airways, breathing/circulatory problems, and is usually  associated with changes in the skin and mucosa . Key triUrticaria must be differentiated from anaphylaxis because the latter may require emergency treatment, including administration of epinephrine . The OnlHere, we describe a child with anaphylaxis exposed to grasses on two occasions.A 4-year-old male was admitted to the ED of our hospital with generalized urticaria. He had been fishing with his father and, 10 min after lying down on grass, he experienced generalized itchy urticaria and angioedema. Thirty minutes later, on examination in the Emergency Department, urticaria was observed over the entire body and angioedema in his hands.Benadryl and methylprednisolone were administered. Four hours after treatment, symptoms had stopped, and urticaria and angioedema had disappeared completely.Two weeks later, he visited a farm. Fifteen minutes after lying down on grass he experienced itching sensations in his hands, arms and trunk, along with generalized erythema. He also complained of congestion/itching in his nose, itchy eyes, and crying. His mother stated that he had slight dyspnea with cough and dizziness. She gave him Benadryl and betamethasone via the oral route based on advice proffered in a previous admission to the ED (which improved the most severe symptoms within 1 h). He was admitted to the ED after 2 h with only erythema, and he received antihistamines for an additional 5 days. The patient had no history of atopy.By anamnesis, an etiology of insect bites, or intake of food or drugs before these two episodes were excluded. Total Immunoglobulin-E was 123 IU/mL, the blood count and complement  were normal. Parasitology studies were negative. His mother and maternal grandmother reported a history of allergic rhinitis.Cynodon dactylis, Phalaris arundinacea and Festuca elatior. Surprisingly these allergens were significantly positive (largest diameter (in mm): 8, 12 and 23, respectively), compared with that of the negative control (1 mm)  were positive to Dermatophagoides farinae, D. pteronyssinus, cat dander, dog dander, Alternaria spp., Aspergillus spp., Mucor spp., Cladosporium spp., Penicillium spp., Rhizopus spp., other grasses, weeds, tree pollens).SPTs were negative for foods  and airborne allergens  with involvement of the skin, respiratory and neurologic compromise . EpinephIt is known that hospital physicians were not knowledgeable regarding current recommendations for anaphylaxis and this could be the reason why our patient didn’t receive epinephrine for his systemic reactions . BecauseLittle is known about pollen sensitization and its potential role in causing urticaria in children. It is known that several types of fruits, vegetables and nuts can cross-react with pollens and cause oral allergy syndrome , but ourAn observational cross-sectional study of 280 patients aged 1–10 years showed a positive SPT to pollen in 3% of those aged 1–4 years . One stuIt had been reported that the prevalence of seasonal allergic rhinitis (SAR) increased markedly after the third birthday, and that the lifetime prevalence of SAR increased significantly from 2.6% at age 2 years to 8.5% at age 5 years, and to 15.3% at age 7 years . In a reOnly a few cases of urticaria/angioedema associated with pollen sensitization have been reported. The case reported here is particularly interesting because, to the best of our knowledge, anaphylaxis due to pollen exposure in children aged <4 years has not been reported before.We strongly encourage all physicians searching for the cause of acute urticaria  to consider the possibility of pollen allergy, and to screen these patients for pollen sensitization. Further studies should be undertaken to determine the prevalence of pollen sensitization in those aged <4 years and the relationship with acute urticaria.