Metabolic Turnover of Synaptic Proteins: Kinetics, Interdependencies and Implications for Synaptic Maintenance

Chemical synapses contain multitudes of proteins, which in common with all proteins, have finite lifetimes and therefore need to be continuously replaced. Given the huge numbers of synaptic connections typical neurons form, the demand to maintain the protein contents of these connections might be expected to place considerable metabolic demands on each neuron. Moreover, synaptic proteostasis might differ according to distance from global protein synthesis sites, the availability of distributed protein synthesis facilities, trafficking rates and synaptic protein dynamics. To date, the turnover kinetics of synaptic proteins have not been studied or analyzed systematically, and thus metabolic demands or the aforementioned relationships remain largely unknown. In the current study we used dynamic Stable Isotope Labeling with Amino acids in Cell culture (SILAC), mass spectrometry (MS), Fluorescent Non-Canonical Amino acid Tagging (FUNCAT), quantitative immunohistochemistry and bioinformatics to systematically measure the metabolic half-lives of hundreds of synaptic proteins, examine how these depend on their pre/postsynaptic affiliation or their association with particular molecular complexes, and assess the metabolic load of synaptic proteostasis. We found that nearly all synaptic proteins identified here exhibited half-lifetimes in the range of 2-5 days. Unexpectedly, metabolic turnover rates were not significantly different for presynaptic and postsynaptic proteins, or for proteins for which mRNAs are consistently found in dendrites. Some functionally or structurally related proteins exhibited very similar turnover rates, indicating that their biogenesis and degradation might be coupled, a possibility further supported by bioinformatics-based analyses. The relatively low turnover rates measured here (∼0.7% of synaptic protein content per hour) are in good agreement with imaging-based studies of synaptic protein trafficking, yet indicate that the metabolic load synaptic protein turnover places on individual neurons is very substantial

Developmental Roles of the Hog1 Protein Phosphatases of the Maize Pathogen <i>Cochliobolus heterostrophus</i>

Protein phosphorylation cascades are universal in cell signaling. While kinome diversity allows specific phosphorylation events, relatively few phosphatases dephosphorylate key signaling proteins. Fungal mitogen activated protein kinases (MAPK), in contrast to their mammalian counterparts, often show detectable basal phosphorylation levels. Dephosphorylation, therefore, could act as a signal. In Cochliobolus heterostrophus, the Dothideomycete causing Southern corn leaf blight, ferulic acid (FA)—an abundant phenolic found in plant host cell walls—acts as a signal to rapidly dephosphorylate the stress-activated MAP kinase Hog1 (High Osmolarity Glycerol 1). In order to identify the protein phosphatases responsible, we constructed mutants in Hog1 phosphatases predicted from the genome by homology to yeast and other species. We found that Cochliobolus heterostrophus mutants lacking PtcB, a member of the PP2C family, exhibited altered growth, sporulation, and attenuated dephosphorylation in response to FA. The loss of the dual-specificity phosphatase CDC14 led to slow growth, decreased virulence, and attenuated dephosphorylation. Mutants in two predicted tyrosine phosphatase genes PTP1 and PTP2 showed normal development and virulence. Our results suggest that a network of phosphatases modulate Hog1’s dual phosphorylation levels. The mutants we constructed in this work provide a starting point to further unravel the signaling hierarchy by which exposure to FA leads to stress responses in the pathogen

Comparisons of metabolic half-life estimates for proteins localized to particular synaptic compartments.

<p>Groups of well characterized proteins were curated manually and estimates of their metabolic half-lives were compared. Each dot represents the half-life value of one protein. Horizontal bars represent average values for each group. The coefficient of variation for each group is provided above each group. Proteins contained in each group along with estimates of their metabolic half-lives are listed below the graph. Except for the difference between the Synaptic Vesicle and Cytoskeleton of Active Zone groups (p = 0.01) all other differences between groups were not statistically significant (Kolmogorov-Smirnov test).</p

Distributions of metabolic half-life estimates.

<p><b>A)</b> Distribution of metabolic half-life estimates for all identified proteins for which fractional incorporation data was obtained for all four time points. Proteins for which fits to single exponentials were not satisfactory (∼2%) were excluded. <b>B)</b> Distribution of metabolic half-life estimates for 191 synaptic proteins (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063191#pone-0063191-t001" target="_blank">Table 1</a>).</p

Relationships between half-life estimates and protein-protein interaction groups.

<p><b>A)</b> A molecular interaction network of 191 synaptic related proteins (main text and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063191#pone-0063191-t001" target="_blank">Table 1</a>) generated on the basis of a manually curated public domain protein-protein interaction database (Human Integrated Protein-Protein Interaction Reference, or HIPPIE; <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063191#pone.0063191-Schaefer1" target="_blank">[55]</a>; see Materials and Methods for further details). Each circle represents one protein, with the estimated metabolic half-life for that protein color coded according to the legend at the bottom left corner. Proteins in each cluster are listed in clockwise fashion, with the top protein in each list referring to the circle in each cluster encompassed with a thick line. <b>B,C)</b> Differences between metabolic turnover rates are smaller on average for pairs of interacting proteins as compared to pairs of non-interacting proteins. Absolute differences between metabolic half-life estimates for all pairs for which interactions are known to exist were compared to all pairs for which interactions are not known to occur (see main text for details), and the distributions of such differences were plotted for both groups. <b>B)</b> All identified proteins, and <b>C)</b> For the list of synaptic and synaptically related proteins. In both cases, differences between groups were highly significant (p ≪10⁻¹⁰, Kolmogorov-Smirnov test).</p

Measuring metabolic protein turnover by SILAC and MS.

<p><b>A)</b> Illustration of the experimental process. At t = 0, heavy lysine and arginine were added to the media of cortical neurons in primary culture (14 days <i>in vitro</i>). 0, 1, 3 and 7 days afterward, cells were harvested and separated side by side by SDS-PAGE. One such gel (stained with Coomassie Blue) is shown on right. Two lanes were run for each time-point to increase protein amounts. Gels were then cut into 9 slices as indicated, proteins in each slice were digested, and the resulting peptides from each slice and each time point were submitted separately to MS analysis. <b>B)</b> MS spectrogram showing the relative amounts, at three time points, of light (open circles) and heavy (closed circles) populations of two particular peptides from slice 5. <b>C)</b> Heavy AA incorporation rates for two particular proteins (Munc18-1 and CaMKII-β2). Each data point represents the fractional incorporation values averaged for all peptides belonging to these particular proteins at a given time point. All four data points were used for fitting to exponential curves (solid lines), providing estimates of time constants (τ) and half-lives as indicated. Graph on right hand side shows extrapolation of same exponential curves to longer times.</p