Calmodulin Transduces Ca²⁺ Oscillations into Differential Regulation of Its Target Proteins

Diverse physiological processes are regulated differentially by Ca²⁺ oscillations through the common regulatory hub calmodulin. The capacity of calmodulin to combine specificity with promiscuity remains to be resolved. Here we propose a mechanism based on the molecular properties of calmodulin, its two domains with separate Ca²⁺ binding affinities, and target exchange rates that depend on both target identity and Ca²⁺ occupancy. The binding dynamics among Ca²⁺, Mg²⁺, calmodulin, and its targets were modeled with mass-action differential equations based on experimentally determined protein concentrations and rate constants. The model predicts that the activation of calcineurin and nitric oxide synthase depends nonmonotonically on Ca²⁺-oscillation frequency. Preferential activation reaches a maximum at a target-specific frequency. Differential activation arises from the accumulation of inactive calmodulin-target intermediate complexes between Ca²⁺ transients. Their accumulation provides the system with hysteresis and favors activation of some targets at the expense of others. The generality of this result was tested by simulating 60 000 networks with two, four, or eight targets with concentrations and rate constants from experimentally determined ranges. Most networks exhibit differential activation that increases in magnitude with the number of targets. Moreover, differential activation increases with decreasing calmodulin concentration due to competition among targets. The results rationalize calmodulin signaling in terms of the network topology and the molecular properties of calmodulin

Dynamics of Single-Cell Protein Covariation during Epithelial–Mesenchymal Transition

Physiological processes, such as the epithelial–mesenchymal transition (EMT), are mediated by changes in protein interactions. These changes may be better reflected in protein covariation within a cellular cluster than in the temporal dynamics of cluster-average protein abundance. To explore this possibility, we quantified proteins in single human cells undergoing EMT. Covariation analysis of the data revealed that functionally coherent protein clusters dynamically changed their protein–protein correlations without concomitant changes in the cluster-average protein abundance. These dynamics of protein–protein correlations were monotonic in time and delineated protein modules functioning in actin cytoskeleton organization, energy metabolism, and protein transport. These protein modules are defined by protein covariation within the same time point and cluster and, thus, reflect biological regulation masked by the cluster-average protein dynamics. Thus, protein correlation dynamics across single cells offers a window into protein regulation during physiological transitions

Dynamics of Single-Cell Protein Covariation during Epithelial–Mesenchymal Transition

Physiological processes, such as the epithelial–mesenchymal transition (EMT), are mediated by changes in protein interactions. These changes may be better reflected in protein covariation within a cellular cluster than in the temporal dynamics of cluster-average protein abundance. To explore this possibility, we quantified proteins in single human cells undergoing EMT. Covariation analysis of the data revealed that functionally coherent protein clusters dynamically changed their protein–protein correlations without concomitant changes in the cluster-average protein abundance. These dynamics of protein–protein correlations were monotonic in time and delineated protein modules functioning in actin cytoskeleton organization, energy metabolism, and protein transport. These protein modules are defined by protein covariation within the same time point and cluster and, thus, reflect biological regulation masked by the cluster-average protein dynamics. Thus, protein correlation dynamics across single cells offers a window into protein regulation during physiological transitions

Protein/fragment spots in porcine centrifugal drip that discriminate between LDrip versus HDrip plus IP animals.

<p>Protein/fragment spots in porcine centrifugal drip that discriminate between LDrip versus HDrip plus IP animals.</p

ANOVA p value, fold changes (calculated from the mean normalised volumes between the groups that shows the maximum of the changes) and average normalised spot volumes of the 20 (A) and 13 (B) spots characterised by mass spectrometry in the <i>post mortem</i> comparisons respectively in the HDrip (A) and LDrip (B) phenotype.

<p>ANOVA p value, fold changes (calculated from the mean normalised volumes between the groups that shows the maximum of the changes) and average normalised spot volumes of the 20 (A) and 13 (B) spots characterised by mass spectrometry in the <i>post mortem</i> comparisons respectively in the HDrip (A) and LDrip (B) phenotype.</p

Hierarchical clustergram.

<p>It can be seen from Fig 3 that there is a clear separation of samples based on the day of measurement. The major split in the protein profiles is between proteins that are more abundant at day 1 and 3 and those that are more abundant at day 7. The next greatest split separates those proteins which are more abundant at day 1 from those more abundant at day 3. According to the spots that have been identified by mass spectrometry [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150605#pone.0150605.s002" target="_blank">S1 Table</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150605#pone.0150605.t002" target="_blank">Table 2</a> in Di Luca <i>et al</i>., [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150605#pone.0150605.ref015" target="_blank">15</a>]], those more abundant at day 1 and 3 were mainly stress related, energy metabolism and transport proteins, whereas those more abundant at day 7 were mainly structural and energy metabolism proteins. The clustergram does not show clear separation of the three phenotypes (HDrip, LDrip and IP) within individual timepoints.</p