A multi-state model of the CaMKII dodecamer suggests a role for calmodulin in maintenance of autophosphorylation.

Ca2+/calmodulin-dependent protein kinase II (CaMKII) accounts for up to 2 percent of all brain protein and is essential to memory function. CaMKII activity is known to regulate dynamic shifts in the size and signaling strength of neuronal connections, a process known as synaptic plasticity. Increasingly, computational models are used to explore synaptic plasticity and the mechanisms regulating CaMKII activity. Conventional modeling approaches may exclude biophysical detail due to the impractical number of state combinations that arise when explicitly monitoring the conformational changes, ligand binding, and phosphorylation events that occur on each of the CaMKII holoenzyme's subunits. To manage the combinatorial explosion without necessitating bias or loss in biological accuracy, we use a specialized syntax in the software MCell to create a rule-based model of a twelve-subunit CaMKII holoenzyme. Here we validate the rule-based model against previous experimental measures of CaMKII activity and investigate molecular mechanisms of CaMKII regulation. Specifically, we explore how Ca2+/CaM-binding may both stabilize CaMKII subunit activation and regulate maintenance of CaMKII autophosphorylation. Noting that Ca2+/CaM and protein phosphatases bind CaMKII at nearby or overlapping sites, we compare model scenarios in which Ca2+/CaM and protein phosphatase do or do not structurally exclude each other's binding to CaMKII. Our results suggest a functional mechanism for the so-called "CaM trapping" phenomenon, wherein Ca2+/CaM may structurally exclude phosphatase binding and thereby prolong CaMKII autophosphorylation. We conclude that structural protection of autophosphorylated CaMKII by Ca2+/CaM may be an important mechanism for regulation of synaptic plasticity.

Competitive tuning: Competition's role in setting the frequency-dependence of Ca2+-dependent proteins.

A number of neurological disorders arise from perturbations in biochemical signaling and protein complex formation within neurons. Normally, proteins form networks that when activated produce persistent changes in a synapse's molecular composition. In hippocampal neurons, calcium ion (Ca2+) flux through N-methyl-D-aspartate (NMDA) receptors activates Ca2+/calmodulin signal transduction networks that either increase or decrease the strength of the neuronal synapse, phenomena known as long-term potentiation (LTP) or long-term depression (LTD), respectively. The calcium-sensor calmodulin (CaM) acts as a common activator of the networks responsible for both LTP and LTD. This is possible, in part, because CaM binding proteins are "tuned" to different Ca2+ flux signals by their unique binding and activation dynamics. Computational modeling is used to describe the binding and activation dynamics of Ca2+/CaM signal transduction and can be used to guide focused experimental studies. Although CaM binds over 100 proteins, practical limitations cause many models to include only one or two CaM-activated proteins. In this work, we view Ca2+/CaM as a limiting resource in the signal transduction pathway owing to its low abundance relative to its binding partners. With this view, we investigate the effect of competitive binding on the dynamics of CaM binding partner activation. Using an explicit model of Ca2+, CaM, and seven highly-expressed hippocampal CaM binding proteins, we find that competition for CaM binding serves as a tuning mechanism: the presence of competitors shifts and sharpens the Ca2+ frequency-dependence of CaM binding proteins. Notably, we find that simulated competition may be sufficient to recreate the in vivo frequency dependence of the CaM-dependent phosphatase calcineurin. Additionally, competition alone (without feedback mechanisms or spatial parameters) could replicate counter-intuitive experimental observations of decreased activation of Ca2+/CaM-dependent protein kinase II in knockout models of neurogranin. We conclude that competitive tuning could be an important dynamic process underlying synaptic plasticity.

Competitive Tuning Among Ca2+/Calmodulin-Dependent Proteins: Analysis of in silico Model Robustness and Parameter Variability.

INTRODUCTION: Calcium/Calmodulin-dependent (Ca2+/CaM-dependent) regulation of protein signaling has long been recognized for its importance in a number of physiological contexts. Found in almost all eukaryotic cells, Ca2+/CaM-dependent signaling participates in muscle development, immune responses, cardiac myocyte function and regulation of neuronal connectivity. In excitatory neurons, dynamic changes in the strength of synaptic connections, known as synaptic plasticity, occur when calcium ions (Ca2+) flux through NMDA receptors and bind the Ca2+-sensor calmodulin (CaM). Ca2+/CaM, in turn, regulates downstream protein signaling in actin polymerization, receptor trafficking, and transcription factor activation.The activation of downstream Ca2+/CaM-dependent binding proteins (CBPs) is a function of the frequency of Ca2+ flux, such that each CBP is preferentially "tuned" to different Ca2+ input signals. We have recently reported that competition among CBPs for CaM binding is alone sufficient to recreate in silico the observed in vivo frequency-dependence of several CBPs. However, CBP activation may strongly depend on the identity and concentration of proteins that constitute the competitive pool; with important implications in the regulation of CBPs in both normal and disease states. METHODS: Here, we extend our previous deterministic model of competition among CBPs to include phosphodiesterases, AMPAR receptors that are important in synaptic plasticity, and enzymatic function of CBPs: cAMP regulation, kinase activity, and phosphatase activity. After rigorous parameterization and validation by global sensitivity analysis using Latin Hypercube Sampling (LHS) and Partial Rank Correlation Coefficients (PRCC), we explore how perturbing the competitive pool of CBPs influences downstream signaling events. In particular, we hypothesize that although perturbations may decrease activation of one CBP, increased activation of a separate, but enzymatically-related CBP could compensate for this loss, providing a homeostatic effect. RESULTS AND CONCLUSIONS: First we compare dynamic model output of two models: a two-state model of Ca2+/CaM binding and a four-state model of Ca2+/CaM binding. We find that a four-state model of Ca2+/CaM binding best captures the dynamic nature of the rapid response of CaM and CBPs to Ca2+ flux in the system. Using global sensitivity analysis, we find that model output is robust to parameter variability. Indeed, although variations in the expression of the CaM buffer neurogranin (Ng) may cause a decrease in Ca2+/CaM-dependent kinase II (CaMKII) activation, overall AMPA receptor phosphorylation is preserved; ostensibly by a concomitant increase in adenylyl cyclase 8 (AC8)-mediated activation of protein kinase A (PKA). Indeed phosphorylation of AMPAR receptors by CaMKII and PKA is robust across a wide range of Ng concentrations, though increases in AMPAR phosphorylation is seen at low Ng levels approaching zero. Our results may explain recent counter-intuitive results in neurogranin knockout mice and provide further evidence that competitive tuning is an important mechanism in synaptic plasticity. These results may be readily translated to other Ca2+/CaM-dependent signaling systems in other cell types and can be used to suggest targeted experimental investigation to explain counter-intuitive or unexpected downstream signaling outcomes.Tamara Kinzer-Ursem is an Assistant Professor in the Weldon School of Biomedical Engineering. She received her B.S. in Bioengineering from the University of Toledo and her M.S. and Ph.D. degrees in Chemical Engineering from the University of Michigan, and her post-doctoral training in Molecular Neuroscience at the California Institute of Technology. Prior to joining Purdue she was the Head of R&D in Biochemistry at Maven Biotechnologies and Visiting Associate in Chemical Engineering at the California Institute of Technology.Research in the Kinzer-Ursem lab focuses on developing tools to advance quantitative descriptions of cellular processes and disease within three areas of expertise: 1) Using particle diffusivity measurements to quantify biomolecular processes. Particle diffusometry is being used as a sensitive biosensor to detect the presence of pathogens in environmental and patient samples. 2) Development of novel protein tagging technologies that are used to label proteins in vivo to enable quantitative description of protein function and elucidate disease mechanisms. 3) Computational modeling of signal transduction mechanisms to understand cellular processes. Using computational techniques, we have recently described "competitive tuning" as a mechanism that might be used to regulate information transfer through protein networks, with implications in cell behavior and drug target analysis.

An automated workflow for quantifying RNA transcripts in individual cells in large data-sets.

Advanced molecular probing techniques such as single molecule fluorescence in situ hybridization (smFISH) or RNAscope can be used to assess the quantity and spatial location of mRNA transcripts within cells. Quantifying mRNA expression in large image sets usually involves automated counting of fluorescent spots. Though conventional spot counting algorithms may suffice, they often lack high-throughput capacity and accuracy in cases of crowded signal or excessive noise. Automatic identification of cells and processing of many images is still a challenge. We have developed a method to perform automatic cell boundary identification while providing quantitative data about mRNA transcript levels across many images. Comparisons of mRNA transcript levels identified by the method highly correlate to qPCR measurements of mRNA expression in Drosophila genotypes with different levels of Rhodopsin 1 transcript. We also introduce a graphical user interface to facilitate analysis of large data sets. We expect these methods to translate to model systems where automated image processing can be harnessed to obtain single-cell data. The described method: •Provides relative intensity measurements that scale directly with the number of labeled transcript probes within individual cells.•Allows quantitative assessment of single molecule data from images with crowded signal and moderate signal to noise ratios.