Muscle

Dupont, Geneviève; Falcke, Martin; Kirk, Vivien; Sneyd, James

doi:10.1007/978-3-319-29647-0_7

Cited by 3 publications

(5 citation statements)

References 109 publications

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“…For example, for weak stimuli, the rising phase of the intensity function may be markedly slower than the falling phase ( Fig 7C and 7E ; yellow and green traces). This most likely reflects the excitable character of intracellular Ca 2+ signalling [ 30 , 40 ]. For weaker stimulation, it takes longer to reach the threshold for generating a Ca 2+ spike, hence the intensity function grows more slowly.…”

Section: Resultsmentioning

confidence: 99%

“…On the other hand, the variability could stem from the composition of the Ca 2+ signalling apparatus in each cell. At the subcellular level, a Ca 2+ spike often corresponds to a travelling Ca 2+ wave that is shaped by Ca 2+ release from intracellular storage compartments such as the endoplasmic reticulum and Ca 2+ sequestration by Ca 2+ pumps [ 30 , 52 – 54 ]. The spatial arrangement of Ca 2+ releasing channels, Ca 2+ pumps and Ca 2+ buffers strongly affects Ca 2+ waves and therefore Ca 2+ spikes [ 11 ].…”

Section: Discussionmentioning

confidence: 99%

“…To date, the main modelling approach to investigate stochastic Ca 2+ spike generation has been based on numerical solutions of differential equations, both ordinary and partial [ 11 , 12 , 15 , 17 – 30 ]. In these studies, the randomness of Ca 2+ spikes results from the stochastic behaviour of Ca 2+ releasing channels, such as the inositol-1,4,5-trisphosphate (InsP 3 ) receptor (InsP 3 R), and their interactions.…”

Section: Introductionmentioning

confidence: 99%

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A Bayesian approach to modelling heterogeneous calcium responses in cell populations

et al. 2017

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Calcium responses have been observed as spikes of the whole-cell calcium concentration in numerous cell types and are essential for translating extracellular stimuli into cellular responses. While there are several suggestions for how this encoding is achieved, we still lack a comprehensive theory. To achieve this goal it is necessary to reliably predict the temporal evolution of calcium spike sequences for a given stimulus. Here, we propose a modelling framework that allows us to quantitatively describe the timing of calcium spikes. Using a Bayesian approach, we show that Gaussian processes model calcium spike rates with high fidelity and perform better than standard tools such as peri-stimulus time histograms and kernel smoothing. We employ our modelling concept to analyse calcium spike sequences from dynamically-stimulated HEK293T cells. Under these conditions, different cells often experience diverse stimulus time courses, which is a situation likely to occur in vivo. This single cell variability and the concomitant small number of calcium spikes per cell pose a significant modelling challenge, but we demonstrate that Gaussian processes can successfully describe calcium spike rates in these circumstances. Our results therefore pave the way towards a statistical description of heterogeneous calcium oscillations in a dynamic environment.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Bayesian approach to modelling heterogeneous calcium responses in cell populations

et al. 2017

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“…Ca 2+ exchanges between the cytoplasm and the extracellular medium are mediated by the plasma membrane Ca 2+ ATPase (PMCA) that extrudes Ca 2+ out of the cell and by SOCE, a STIM/ORAI-mediated mechanism that is sensitive to the Ca 2+ concentration inside the ER. The resulting changes in ER ( C ER ) and cytosolic ( C c ) Ca 2+ concentrations are given by the following equations ( 47 ):…”

Section: Methodsmentioning

confidence: 99%

Gradual ER calcium depletion induces a progressive and reversible UPR signaling

Pontisso,

Ornelas-Guevara,

Chevet

et al. 2024

PNAS Nexus

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The unfolded protein response (UPR) is a widespread signal transduction pathway triggered by endoplasmic reticulum (ER) stress. Because calcium (Ca2+) is a key factor in the maintenance of ER homeostasis, massive Ca2+ depletion of the ER is a potent inducer of ER stress. Although moderate changes in ER Ca2+ drive the ubiquitous Ca2+ signaling pathways, a possible incremental relationship between UPR activation and Ca2+ changes has yet to be described. Here, we determine the sensitivity and time-dependency of activation of the three ER stress sensors, inositol-requiring protein 1 alpha (IRE1α), protein kinase R-like ER kinase (PERK), and activating transcription factor 6 alpha (ATF6α) in response to controlled changes in the concentration of ER Ca2+ in human cultured cells. Combining Ca2+ imaging, fluorescence recovery after photobleaching experiments, biochemical analyses, and mathematical modeling, we uncover a nonlinear rate of activation of the IRE1α branch of UPR, as compared to the PERK and ATF6α branches that become activated gradually with time and are sensitive to more important ER Ca2+ depletions. However, the three arms are all activated within a 1 h timescale. The model predicted the deactivation of PERK and IRE1α upon refilling the ER with Ca2+. Accordingly, we showed that ER Ca2+ replenishment leads to the complete reversion of IRE1α and PERK phosphorylation in less than 15 min, thus revealing the highly plastic character of the activation of the upstream UPR sensors. In conclusion, our results reveal a dynamic and dose-sensitive Ca2+-dependent activation/deactivation cycle of UPR induction, which could tightly control cell fate upon acute and/or chronic stress.

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“…~100 nM (K D =119 nM reported by Taylor and Konieczny, 2016) and thus act as a buffer of IP 3 . Assuming fast binding and unbinding of IP 3 to and from its receptor, the resulting effective diffusion coefficient (Dupont et al, 2016) is given by: where D represents the IP 3 diffusion coefficient in the absence of buffers, S T the concentration of IP 3 R monomers, and K D the equilibrium dissociation constant of IP 3 from its receptor. S T is cell dependent, in the range of 80 nM to 2 µM (Spät et al, 1986; Wojcikiewicz, 1995; Dupont et al, 2008; Taylor and Konieczny, 2016) with an estimated value of 542 nM in SH-SY5Y cells (Wojcikiewicz, 1995).…”

Section: Introductionmentioning

confidence: 99%

Computational investigation of IP₃ diffusion

Guevara¹,

Gil²,

Voorsluijs³

et al. 2022

Preprint

Self Cite

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Inositol 1,4,5-trisphosphate (IP3) plays a key role in calcium signaling. After stimulation, it diffuses from the plasma membrane where it is produced to the endoplasmic reticulum where its receptors are localized. Based on in vitro measurements, IP3 was long thought to be a global messenger characterized by a diffusion coefficient of ~280 micron2s-1. However, in vivo observations revealed that this value does not match with the timing of localized Ca2+ increases induced by the confined release of a non-metabolizable IP3 analog. A theoretical analysis of these data concluded that in intact cells diffusion of IP3 is strongly hindered, leading to a 30-fold reduction of the diffusion coefficient. Here, we re-investigated the characteristics of IP3 diffusion using a stochastic model of Ca2+ puffs. Our simulations concluded to a moderate decrease of the effective IP3 diffusion coefficient, which quantitatively agrees with a buffering effect by non-fully bound, hence inactive, IP3 receptors. The value of ~100 micron2s-1 predicted by the model is in average not much affected by the presence of the endoplasmic reticulum, which represents an obstacle to the free displacement of molecules, but can be significantly increased when cells display an elongated, 1-dimensional like geometry.

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Muscle

Cited by 3 publications

References 109 publications

A Bayesian approach to modelling heterogeneous calcium responses in cell populations

A Bayesian approach to modelling heterogeneous calcium responses in cell populations

Gradual ER calcium depletion induces a progressive and reversible UPR signaling

Computational investigation of IP₃ diffusion

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Muscle

Cited by 3 publications

References 109 publications

A Bayesian approach to modelling heterogeneous calcium responses in cell populations

A Bayesian approach to modelling heterogeneous calcium responses in cell populations

Gradual ER calcium depletion induces a progressive and reversible UPR signaling

Computational investigation of IP3 diffusion

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Computational investigation of IP₃ diffusion