  1. Computational and Systems Biology
  2. Neuroscience

Reciprocal regulation of ARPP-16 by PKA and MAST3 kinases provides a cAMP-regulated switch in protein phosphatase 2A inhibition

  1. Veronica Musante
  2. Lu Li
  3. Jean Kanyo
  4. Tukiet T Lam
  5. Christopher M Colangelo
  6. Shuk Kei Cheng
  7. A Harrison Brody
  8. Paul Greengard
  9. Nicolas Le Novère
  10. Angus C Nairn 
  1. Yale University School of Medicine, United States
  2. The Babraham Institute, United Kingdom
  3. Yale University School Medicine, United states
  4. The Rockefeller University, United States
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Cite this article as: eLife 2017;6:e24998 doi: 10.7554/eLife.24998

Abstract

ARPP-16, ARPP-19, and ENSA are inhibitors of protein phosphatase PP2A. ARPP-19 and ENSA phosphorylated by Greatwall kinase inhibit PP2A during mitosis. ARPP-16 is expressed in striatal neurons where basal phosphorylation by MAST3 kinase inhibits PP2A and regulates key components of striatal signaling. The ARPP-16/19 proteins were discovered as substrates for PKA, but the function of PKA phosphorylation is unknown. We find that phosphorylation by PKA or MAST3 mutually suppresses the ability of the other kinase to act on ARPP-16. Phosphorylation by PKA also acts to prevent inhibition of PP2A by ARPP-16 phosphorylated by MAST3. Moreover, PKA phosphorylates MAST3 at multiple sites resulting in its inhibition. Mathematical modeling highlights the role of these three regulatory interactions to create a switch-like response to cAMP. Together, the results suggest a complex antagonistic interplay between the control of ARPP-16 by MAST3 and PKA that creates a mechanism whereby cAMP mediates PP2A disinhibition.

https://doi.org/10.7554/eLife.24998.001

Introduction

ARPP-16 and ARPP-19 were originally identified together with DARPP-32 and ARPP-21 as a group of cAMP-regulated phosphoproteins (ARPPs) enriched in striatal neurons (Walaas et al., 1983; Girault et al., 1990; Horiuchi et al., 1990). ARPP-16 and ARPP-19 are alternatively spliced variants, with ARPP-19 containing 16 additional amino acids at its N-terminus. ARPP-16/19 are also related to endosulfine (ENSA or ARPP-19e), a distinct gene product that shares high identity with the common region of ARPP-16/19 but contains a unique N-terminal region (Horiuchi et al., 1990; Peyrollier et al., 1996; Heron et al., 1998; Dulubova et al., 2001). ARPP-16 is present only in the brain and is highly expressed in medium spiny neurons (MSNs) in striatum (Girault et al., 1990). In contrast, ARPP-19 is ubiquitously expressed with low levels in striatum (possibly also in non-neuronal cells). ENSA is expressed in all tissues, is widely distributed in the brain, and represents the predominant proportion of the 19 kDa forms of the ARPP-19/ENSA proteins found in striatum (Dulubova et al., 2001).

The ARPP-16/19/ENSA proteins have become the focus of many studies in the past few years as a result of their discovery as potent inhibitors of the serine/threonine protein phosphatase, PP2A. PP2A exists in eukaryotic cells as a heterotrimer of a catalytic C subunit, a scaffolding A subunit, and a variable B subunit that serves to influence the substrate specificity of the phosphatase (Janssens and Goris, 2001; Shi, 2009). PP2A heterotrimers play critical roles in diverse signaling pathways including regulation of the cell cycle, cell proliferation, and neuronal signalling, and deficits in PP2A function are implicated in many human diseases including cancer, Alzheimer’s disease and major depressive disorder (Sontag and Sontag, 2014; Network and Pathway Analysis Subgroup of Psychiatric Genomics Consortium, 2015; Sangodkar et al., 2016). Studies of cell cycle regulation in frog oocytes had found that PP2A including the B55 subunit directly dephosphorylates Cdk substrates upon mitotic exit and that inhibition of PP2A-B55 by a mechanism involving Greatwall (Gwl) kinase was required during mitosis (Castilho et al., 2009; Mochida et al., 2009; Vigneron et al., 2009). Subsequent work revealed that ARPP-19 and ENSA when phosphorylated by Gwl act as critical PP2A inhibitors (Gharbi-Ayachi et al., 2010; Mochida et al., 2010).

In mammalian cells, MASTL (for microtubule-associated serine/threonine kinase like) is functionally equivalent to Gwl (Voets and Wolthuis, 2010), and Gwl/MASTL are now known to play an important role in mitotic progression in several model systems, including Drosophila melanogaster, Xenopus laevis and mammalian cell lines (Yu et al., 2004, 2006; Archambault et al., 2007; Burgess et al., 2010; Voets and Wolthuis, 2010; Glover, 2012; Lorca and Castro, 2012; Álvarez-Fernández et al., 2013; Wang et al., 2013). Additional functions have also been reported for ENSA/ARPP-19 proteins. Genetic studies in flies and biochemical studies in Xenopus suggest that ARPP-19 is also involved in meiotic division (Von Stetina et al., 2008; Rangone et al., 2011; Wang et al., 2011; Dupré et al., 2013). In budding yeast, inhibition of PP2A-B55 by Igo1/2 (the equivalent of the ARPP proteins) leads to an increased phosphorylation of Gis1, a transcription factor important for cellular quiescence after glucose deprivation (Bontron et al., 2013). We have also recently identified ARPP-16 as a PP2A inhibitor in mammalian brain when phosphorylated at Ser46 by MAST3 kinase (Andrade et al., 2017), a homolog of MASTL/Gwl, which is active in post-mitotic neurons and is highly expressed in striatum (Garland et al., 2008). Conditional knockout of ARPP-16 in mouse forebrain has functional effects on dopaminergic signaling via DARPP-32 and ERK/MAPK, and leads to altered behavioral responses (Andrade et al., 2017).

Together, these various studies clearly identify ARPP-16, ARPP-19 and ENSA as important components of cellular signaling in a variety of cell types and organisms. However, several important questions remain to be answered. Our studies in striatal neurons indicate that Ser46 is phosphorylated to a high basal stoichiometry by MAST3 (Andrade et al., 2017), which is opposite to that found in non-mitotic phases in dividing cells (Dupré et al., 2013, 2014). Notably, the function of phosphorylation of the ARPP/ENSA proteins by PKA is not known. In striatal cell preparations, activation of PKA leads to increased phosphorylation of Ser88 of ARPP-16, but concomitantly to substantial dephosphorylation at Ser46 of ARPP-16 (and also the equivalent Ser62 of ENSA) (Andrade et al., 2017). These results suggest that rather than regulate ARPP-16 activity directly, cAMP-dependent signaling may serve to attenuate phosphorylation of Ser46 leading to disinhibition of PP2A. Related to this possibility, ARPP-19 phosphorylated by PKA was found to be a key element for maintaining prophase arrest (Dupré et al., 2014).

In the current study, we investigated the regulatory roles of ARPP-16 phosphorylation by MAST3 and PKA using a combination of biochemical and modeling methods. The results indicate that a mutually antagonistic relationship exists between phosphorylation of Ser46 by MAST3 and Ser88 by PKA that serves as a potential toggle switch mechanism. Moreover, PKA is able to phosphorylate MAST3 leading to its inhibition. The complex interplay likely serves to underlie the balance of signaling pathways that act via ARPP/ENSA and PP2A in distinct ways in either dividing cells or non-dividing neurons.

Results

Mutually antagonistic relationship between ARPP-16 phospho-sites Ser46 and Ser88 in vitro

We initially examined in in vitro assays if prior phosphorylation of either Ser46 by MAST3 or Ser88 by PKA influenced the ability of ARPP-16 to be phosphorylated by the alternative kinase. Recombinant ARPP-16 was first phosphorylated at Ser88 by PKA to a maximal stoichiometry and then used as substrate for MAST3 (Figure 1). Prior phosphorylation of Ser88 slowed the phosphorylation of Ser46 by MAST3 (Figure 1a). Kinetic analysis indicated that the prior phosphorylation at Ser88 reduced the enzyme Vmax and increased the Km, effectively reducing the ‘catalytic efficiency’ by a factor of 3 (Figure 1b and Table 1). A similar reduction in the efficiency of phosphorylation of Ser46 by MAST3 was observed when a phospho-mimetic version or ARPP-16, S88D-ARPP-16, was used as a substrate (Figure 1—figure supplement 1). We also performed the converse analysis where ARPP-16 was first maximally phosphorylated by MAST3 at Ser46 and then used as substrate for PKA (Figure 1c,d). The prior phosphorylation of Ser46 resulted in slower phosphorylation of Ser88 by PKA, which was reflected in ~7 fold reduction in Vmax, although notably there was a ~2 fold decrease in Km. (Table 1). There was no difference in the ability of PKA to phosphorylate Ser88 in S46D-ARPP-16 (data not shown), indicating that the S46D mutation did not act as a phospho-mimetic.

Figure 1 with 1 supplement see all
Table 1

cAMP-dependent regulation of ARPP-16 phosphorylation on Ser46 and Ser88 in intact cells

ARPP-16 is only highly expressed in striatum, where its levels are low at birth and continue to rise postnatally for several weeks (Girault et al., 1990). MAST3 is also selectively expressed in striatum (Garland et al., 2008). To further investigate the regulation of ARPP-16 phosphorylation in intact cells, we considered the use of various preparations including acutely isolated striatal slices, primary cultures enriched for striatal neurons, or cell culture. Acutely prepared striatal slices are an ex vivo preparation that are useful for studies that involve pharmacological manipulation (Andrade et al., 2017) but are not easily amenable to molecular manipulation. In primary neuronal cultures, there was no detectable expression of ARPP-16 and only low levels of expression of ENSA compared to that found in adult striatum (not shown). Similarly, the expression of MAST3 was very low in cultured striatal neurons compared to adult striatum (not shown).

We therefore decided to generate a cell-based model using HEK293T cells in which ARPP-16 and MAST3 were expressed at levels similar to that found in adult striatum. Using the HEK293T cell model, we first investigated the interaction of the Ser46 and Ser88 phosphorylation sites. Under basal conditions, the phosphorylation of Ser46 was low (Figure 2, panel a, lane 1) reflecting low activity of endogenous MAST-related kinases. The phosphorylation of Ser88 was also very low reflecting low basal cAMP levels (lane 1). Forskolin treatment led to a marked increase in phosphorylation of Ser88 (lane 2). Expression of HA-MAST3 led to a marked increase in phosphorylation of Ser46 (lane 3). However, the phosphorylation of Ser46 in the presence of MAST3 expression was significantly reduced by forskolin treatment compared to untreated, but MAST3-expressing cells (lane 4). There was a small but not statistically significant effect of MAST3 expression on the level of phosphorylation of Ser88 following forskolin treatment (lane 4). We also expressed separately S46D-ARPP-16 and S88D-ARPP-16. The increase in phosphorylation of Ser88 observed in the presence of forskolin was unaffected in the S46D-ARPP-16 mutant (lanes 5 and 6). However, there was no increase in phosphorylation of Ser46 in S88D-ARPP-16 when MAST3 was over-expressed (lanes 7 and 8). These results are consistent with the in vitro studies shown in Figure 1, and together, indicate that PKA-mediated phosphorylation of Ser88 suppresses Ser46-phosphorylation by MAST3, while MAST3-mediated phosphorylation of Ser46 suppresses PKA-mediated phosphorylation of Ser88.

Figure 2 with 1 supplement see all

Mathematical modeling supports a switch-like inhibition of PP2A by ARPP-16

To investigate whether mutual inhibition between the two phosphorylation sites on ARPP-16 is capable of producing a switch-like response to cAMP level changes, we set up a mathematical model focusing on the reciprocal inhibition of MAST3 and PKA by mutually antagonist phosphorylation sites in ARPP-16 (see Materials and methods and Appendix 1—the mutual inhibition model). Steady-state levels of P-S46 and P-S88 ARPP-16 were studied in a two-variable phase-plane (Figure 3a). The ensemble of all the system states where a variable does not change over time form a curve called a nullcline. Whenever the P-S46 nullcline intersects the P-S88 one at a given cAMP concentration, both phosphorylation sites are at steady states. As a result of the mutual inhibition, high phosphorylation of Ser46 was associated with low phosphorylation on Ser88 and vice versa (P-S46 nullcline, blue curve). However, the shape of the P-S88 nullcline (red curves) depends on cAMP concentration. An increase of cAMP concentration activates PKA and shifts the nullcline of P-S88 to the right. At low cAMP, there is only one stable steady state corresponding to the full phosphorylation at Ser46 and close to zero phosphorylation at Ser88, as Ser46 phosphorylation suppresses basal PKA activity. At highest cAMP concentration, only one stable steady state exists where ARPP-16 is almost fully phosphorylated at Ser88 with only sub-micromolar phosphorylation at Ser46. A bistable situation arises at intermediate cAMP concentrations, the two stable states co-existing with an unstable one.

Figure 3 with 7 supplements see all

Bifurcation plots of both Ser88 and Ser46 in response to cAMP also showed two qualitatively different states that overlap at intermediate levels of cAMP, indicating bistability within this range of cAMP concentration (Figure 3b). When cAMP concentration increases to more than 3.5 μM, Ser46 phosphorylation abruptly drops whereas Ser88 phosphorylation suddenly increases. In the opposite direction, when cAMP decreases the switch occurs below 1.5 µM creating a hysteresis. Based on the mutually antagonistic relationship between the phosphorylation sites on ARPP-16, our model predicts a switch-like behavior of their phosphorylation states in response to changes in cAMP level.

This switch-like response to cAMP concentration change was also detected in experiments where mouse striatal brain slices, an ex vivo model, were used to assess the effect of stimulation of PKA in a more physiological context. In striatal slices, ARPP-16 and ENSA were phosphorylated at high basal levels by MAST3 (Figure 2—figure supplement 1). Stimulation with SKF81297 (a D1 receptor agonist), like forskolin, led to an increase in Ser88 phosphorylation which was accompanied by decreased phosphorylation of Ser46.

PKA phosphorylates and regulates MAST3 kinase

The above studies with HEK293T cells and the in vitro assays indicate that MAST3 is basally active. We next investigated if PKA might be able to phosphorylate and regulate MAST3. MAST3-HA was expressed in HEK293T cells and isolated by immunoprecipitation. MAST3-HA was incubated with PKA in the presence of 32P-ATP. PKA phosphorylated MAST3 rapidly reached a plateau after ~10 min (Figure 4a). We then examined the effect of PKA-dependent phosphorylation on MAST3 activity using ARPP-16 as substrate for MAST3. PKA-phosphorylated MAST3 was much less active than control MAST3 (Figure 4b).

To investigate the regulation of MAST3 further, and identify directly sites of phosphorylation, we expressed MAST3-HA in HEK293T cells and incubated them without or with forskolin. MAST3-HA was then immunoprecipitated, samples digested, phospho-peptides enriched with TiO2, and peptides identified by LC-MS/MS (Supplementary file 1). A total of 12 phospho-sites were identified under control conditions. Of these, 6 were found also in the forskolin samples, while an additional 7 phospho-sites were found only in the samples incubated with forskolin. Notably, inspection of the amino acid sequence of MAST3 using bioinformatics tools KinasePhos (Huang et al., 2005), PhosphoNet (Safaei et al., 2011), NetPhos 3.1 (Blom et al., 2004) also identified Thr389 (RHRDT[389]RQR) as a potential PKA site. Although MS/MS analysis of a chymotryptic digest of MAST3-HA showed the presence of a phospho-peptide containing T389 the level of this peptide was very low and we did not observe the presence of this phosphorylated site in any tryptic digestion. We then assessed the effects of forskolin on the phosphorylation of Ser512, Thr628 and Ser747 in different experiments. We used a label-free approach to reanalyze some of the initial samples, and found a 4.8-fold increase in Ser512, 47-fold increase in Thr628, and 15-fold increase in Ser747 (Figure 4c). In a second set of experiments, the data-independent SWATH MS/MS technique was used to quantify the effect of incubation with forskolin. An increase in phosphorylation of Ser512, Thr628 and Ser747 of 1.2–2.5 fold was found (average of two separate experiments). Finally, using a label-free approach in a new set of samples, increases of 1.6-fold in Thr628, and 7.1-fold in Ser747 were observed, although Ser512 was recovered in low amounts and no significant changes were observed. Together, the results suggest that MAST3 is phosphorylated at multiple sites by PKA.

cAMP/PKA-dependent regulation of MAST3 kinase in intact cells

We investigated the potential role of phosphorylation of four sites, Thr389, Ser512, Thr628 and Ser747 through expression of WT MAST3-HA, and mutants in which each site was mutated to either alanine or aspartate to mimic non-phosphorylatable or potential phosphomimetic forms, respectively. We focused first on the effect of Thr389 phosphorylation. We immunoprecipitated WT, T389D-MAST3 and T389A-MAST3 from HEK293T cells, and carried out in vitro assays to directly assess the effects of PKA-dependent phosphorylation on MAST3 activity (Figure 5a). Equivalent levels of MAST3 were recovered by immunoprecipitation. However, the T389D-MAST3-HA mutant was much less active than WT-MAST3-HA. In contrast, even after pre-incubation with PKA and ATP, the T389A-MAST3-HA mutant was only slightly less active than WT MAST3-HA. We next expressed ARPP-16 in HEK293T cells in combination with WT-, T389D- or T389A-MAST3 and incubated without or with forskolin (Figure 5b,c). As shown in Figure 2 for WT MAST3-HA, forskolin treatment led to an increase in Ser88 phosphorylation and a decrease in Ser46 phosphorylation. Cells expressing T389D-MAST3 exhibited a large reduction in the phosphorylation of Ser46 compared to WT-MAST3. In cells expressing T389A-MAST3 in the absence of forskolin, phosphorylation of Ser46 was similar to WT MAST3-HA. In comparison to WT MAST3-HA, addition of forskolin resulted in a smaller reduction in Ser46 phosphorylation. Phosphorylation of Ser88 was increased following incubation with forskolin for either WT or T389A-MAST3-HA.

Phosphorylation of Ser46 was reduced for the S512D mutant but not as dramatically as for the T389D mutant (Figure 6a,b). For the S512A mutant the effect of forskolin on Ser46 phosphorylation was similar to that observed for WT MAST3-HA. Ser88 phosphorylation was stimulated by forskolin to a similar level for the S512A mutant. For the T628 mutants, phosphorylation of Ser46 was low for the T628D mutant (Figure 6c,d) but not as low as for the T389D mutant. Addition of forskolin resulted in a reduction in Ser46 phosphorylation to a level comparable to WT MAST3-HA. Phosphorylation of Ser88 was increased following incubation with forskolin for either WT or T628A-MAST3-HA. In contrast, mutation of Ser747 to aspartate had no significant effect on Ser46 phosphorylation (Figure 6c,d). Moreover, mutation to alanine did not prevent the ability of forskolin to reduce phosphorylation of Ser46.

Taken together, these results supported the conclusion that MAST3 is regulated by phosphorylation at three of the four sites analyzed, namely Thr389, Ser512 and Thr628. The in vitro and intact cell experiments indicated that phosphorylation of Thr389 had the most robust effect on MAST3 activity. Mutation to alanine largely prevented the effect of PKA in the in vitro assay and mutation to a phospho-mimetic residue decreased phosphorylation of ARPP-16 to a very low level at Ser46 in the intact cell experiments. In the intact cell experiments, mutation of Ser512 or Thr628 to aspartate had lesser effects on Ser46 phosphorylation than the T389D mutation. Moreover, the Ser512 or Thr628 to alanine mutants had no effect, presumably because of the predominant effect of Thr389 phosphorylation.

We then studied how this direct PKA regulation of MAST3 might affect the switch control over PP2A activity, by extending the mathematical model and examining the dynamic behaviour (see Materials and methods and Appendix 1—the mutual inhibition plus PKA inhibits MAST3 model). The P-S46-ARPP-16 bifurcation diagram indicated that the cAMP concentration range for bistability becomes narrower and shifts towards lower cAMP levels compared with the mutual antagonism scenario alone (Figure 3—figure supplement 1). In addition, with this regulation, P-S46-ARPP-16 is dephosphorylated to a much lower level at higher cAMP concentration, therefore, the disinhibition of PP2A at a high cAMP level is more potent (Figure 3—figure supplement 2).

Phosphorylation of ARPP-16 at Ser88 influences the inhibition of PP2A by phospho-Ser46

Our previous in vitro studies have shown that phosphorylation of ARPP-16 at Ser88 has no effect on PP2A activity. We further analyzed in vitro the influence of Ser88 phosphorylation on the ability of P-S46-ARPP-16 to inhibit PP2A. Recombinant ARPP-16 was thiophosphorylated in vitro at Ser46 to avoid dephosphorylation by PP2A (Gharbi-Ayachi et al., 2010; Mochida, 2014; Andrade et al., 2017). The activity of immunoprecipitated PP2A-B55α complex was measured using a phospho-peptide substrate and the malachite green assay method. As shown previously, P-S46-ARPP-16 inhibited PP2A-B55α (Figure 7). Addition of an equimolar amount of dephospho-ARPP-16 had no effect on the ability of P-S46-ARPP-16 to inhibit PP2A. However, addition of an equimolar amount of P-S88-ARPP-16 blocked the inhibitory effect of P-S46-ARPP-16. Addition of an equimolar amount of the S88D-ARPP-16 mutant partially attenuated the effect of P-S46-ARPP-16. These results suggest that in addition to antagonizing the ability of MAST3 to phosphorylate ARPP-16 at Ser46, phospho-Ser88-ARPP-16 may act in a ‘dominant-negative’ manner to antagonize the inhibition of PP2A by phospho-Ser46-ARPP-16.

Using the mathematical model, we asked how this ‘dominant-negative’ regulation affects the switch-like control over PP2A activity. We first added this regulation directly into the mutual-antagonism model, assuming that P-S88-ARPP-16 weakens the binding between P-S46-ARPP-16 and PP2A (see Materials and methods and Appendix 1—the mutual inhibition plus PKA inhibits MAST3 and dominant negtive model with modifications in red). The bifurcation diagram of P-S46-ARPP-16 showed that by adding this ‘dominant-negative’ mode of regulation, the cAMP concentration range required for bistability shifts towards higher cAMP levels (Figure 3—figure supplement 1). Indeed, by interfering with PP2A binding, P-S88-ARPP-16 prevents P-S46-ARPP-16 dephosphorylation. Therefore, higher cAMP concentrations are required for both switching off and switching on Ser46 phosphorylation. As P-S88-ARPP-16 antagonizes the inhibition of PP2A, and by doing so preventing P-S46-ARPP-16 dephosphorylation, PP2A disinhibition is reduced to a greater extent than the level of Ser46 dephosphorylation (Figure 3—figure supplement 2).

We then added the direct regulation of PKA on MAST3 back into the model. The switch-like control of cAMP over PP2A activity was intact. However, combining all three modes of regulation together resulted in only slight modification of PP2A disinhibition, compared to the model with only mutual antagonism and PKA inhibiting MAST3 (Figure 3—figure supplements 1 and 2). Based on the data we have and the assumptions we made, this would indicate that the direct regulation from PKA to MAST3 has a much potent effect on controlling PP2A activity than the ‘dominant-negative’ effect of P-S88-ARPP-16 on PP2A inhibition.

Discussion

Recent studies have identified the ARPP/ENSA proteins as important regulators of protein phosphatase PP2A (Gharbi-Ayachi et al., 2010; Mochida et al., 2010; Dupré et al., 2013; Andrade et al., 2017). The ARPP/ENSA proteins are remarkably conserved in plants, fungi, yeast, flies, worms and mammals (Dulubova et al., 2001; Labandera et al., 2015) suggesting a primal origin and conserved function to control PP2A activity. However, the precise role of the ARPP/ENSA proteins varies in different cellular contexts, and the proteins are subject to variable modes of regulation. In frog oocytes, ARPP-19 is highly phosphorylated at the PKA site (Ser109, the site equivalent to Ser88 in ARPP-16) during prophase arrest while phosphorylation of Ser67 by Gwl is very low (Dupré et al., 2013, 2014). Upon resumption of meiosis, reduced PKA activity and dephosphorylation of Ser109 is required, following which Ser67 phosphorylation by Gwl acts within an amplification mechanism, converting ARPP-19 in to a potent inhibitor of PP2A (Dupré et al., 2013, 2014). In contrast, in non-dividing neurons in striatum, MAST3 activity is basally active leading to high phosphorylation of Ser46 of ARPP-16, and constitutive inhibition of PP2A (Andrade et al., 2017). Correspondingly, phosphorylation of Ser88 in ARPP-16 is low in neurons, but in response to cAMP-dependent activation of PKA, Ser88 phosphorylation is increased while Ser46 phosphorylation decreases, leading to dis-inhibition of PP2A (Andrade et al., 2017). That the same conserved signaling module is used in such distinct roles, where the signaling mechanism is essentially inverted, suggests a complex series of interactions between the protein kinases and phosphatases that regulate the phosphorylation of ARPP/ENSA proteins. The results from the current in vitro, cell-based, and modelling studies of ARPP-16, indicate that there is a mutually antagonistic relationship between the phosphorylation of the PKA and MAST3 sites that contribute to a switch-like process that likely controls PP2A regulation in distinct ways in dividing and non-dividing cells (Figure 8).

In vitro kinetic analyses indicate that prior phosphorylation of either Ser46 or Ser88 in ARPP-16 acts to suppress the phosphorylation of the other site by its respective kinase, MAST3 or PKA. Studies in intact cells were consistent with the in vitro results. Mutation of Ser88 to aspartate acted as a phospho-mimetic, generating a substrate that was phosphorylated ~5 fold less efficiently by MAST3 in vitro and in intact cells. In contrast, mutation of Ser46 to aspartate did not create a phospho-mimetic in terms of phosphorylation of Ser88 by PKA, consistent with the fact that a Ser46 to aspartate mutation in ARPP-16 failed to inhibit PP2A (Musante unpublished results). The in vitro kinetic assays revealed that prior phosphorylation of either Ser88 or Ser46 influenced both the Km and Vmax parameters by MAST3 or PKA, respectively. ARPP-16, ARPP-19 and ENSA, like DARPP-32 and ARPP-21 are heat-stable proteins that contain little secondary structure and are likely to be highly flexible (Huang et al., 2001; Dancheck et al., 2008). Presumably, phosphorylation of either Ser46 or Ser88 in ARPP-16 has the ability to constrain the structure(s) of the protein to negatively influence the binding that is reflected in the altered kinetic parameters measured for MAST3 or PKA. Interestingly, injection of a S109D-ARPP-19 protein into intact frog oocytes could not be phosphorylated by Gwl at Ser67 and arrested oocytes in prophase (Dupré et al., 2014). This suggests that the mechanism of action of PKA phosphorylation of ARPP/ENSA proteins may be common to both MAST3 and Gwl.

Though initially described as a microtubule-associated protein, little is known about the function or regulation of the MAST family of protein kinases (Walden and Cowan, 1993; Zhou et al., 2004; Valiente et al., 2005; Wang et al., 2006). In addition to a conserved kinase domain, MAST1-4 contain a PDZ domain of unknown function, as well as other potential regions that may influence cellular localization or action. In the current study, we found that PKA directly phosphorylates MAST3, leading to inhibition of enzyme activity. Mass spectrometry identified numerous phosphorylation sites including Thr389, Ser512 and Thr628, that contribute to inhibition of MAST3 to different extents, with Thr389 having the largest effect. These 3 sites are all within the kinase domain (residues 367–640 of MAST3) and may influence enzyme activity via direct or allosteric effects.

Thr389 (MAST1, 2, 3, 4), Ser512 (MAST1, 2, 3, 4 and MASTL) and Thr628 (MAST2, 3, 4) are present in other MAST kinase isoforms suggesting conserved modes of regulation of other family members by PKA. In mammals, MASTL kinase is likely widely distributed in dividing cells where it regulates cell cycle progression via the inhibition of ENSA/ARPP-19 (Voets and Wolthuis, 2010). Phosphorylation of MASTL by PKA might allow for cAMP-dependent regulation of cell division (see (Dupré et al., 2014) for discussion). MAST1-4 exhibit variable patterns of expression in mammalian organs and tissues (Garland et al., 2008), where they presumably phosphorylate ENSA/ARPP-19/ARPP-16, and potentially other substrates. In particular, the different MAST kinase isoforms show distinct patterns of expression in the brain, where they could be regulated by different G protein-coupled receptors that modulate cAMP/PKA signalling (Gainetdinov et al., 2004). It will be important to further investigate how MAST3 and the other MAST kinases are regulated by PKA and other phosphorylation events. Currently, there is nothing known about the identity of any of the phosphatase(s) responsible for dephosphorylation of MAST3. Recent studies of Gwl and MASTL have identified specific roles of both PP1 and PP2A in their regulation (Heim et al., 2015; Ma et al., 2016; Rogers et al., 2016; Wang et al., 2016). Modeling studies also support a potential role for phosphatase action upon Gwl as part of a negative feedback process that could contribute to its regulation (Vinod and Novak, 2015)(see also below). MAST3 and the other MAST kinase isoforms are also likely to be subject to variable modes of regulation at different sites by specific protein phosphatases.

As different MAST kinase isoforms, expressed in distinct patterns in the brain, could regulate the inhibitory effect of ARPP-16/ARPP-19/ENSA on PP2A, we used the model to test how different concentrations of total MAST3 might affect the switch-like control over PP2A inhibition, in response to cAMP concentration changes. Varying total MAST3 concentration in the mathematical model with all three layers of regulation does not affect the general switch-like response to cAMP concentration changes (Figure 3—figure supplement 3). In fact, at a fixed cAMP level, bistability exists over a range of MAST3 concentrations (Figure 3—figure supplement 4). A two-variable bifurcation diagram reveals wide dynamic ranges for joint MAST3 and cAMP concentrations where bistability exists (Figure 3—figure supplement 5).

The structural details of how ARPP-16/ARPP-19/ENSA interact with and inhibit PP2A are not known. ARPP-19/ENSA phosphorylated by Gwl (Gharbi-Ayachi et al., 2010; Mochida et al., 2010; Mochida, 2014) or ARPP-16 phosphorylated by MAST3 (Andrade et al., 2017) act as specific inhibitors of heterotrimeric but not AC dimeric forms of PP2A. Studies in Xenopus oocytes suggest a preference for binding of phospho-S67-ENSA to the B55-containing form of the phosphatase (Castilho et al., 2009; Mochida et al., 2010), while phospho-S46-ARPP-16 can inhibit both B55α and B56δ-containing PP2A heterotrimers (Andrade et al., 2017). The interaction of phospho-S46/S67-ARPP-16/19/ENSA with PP2A likely involves some sort of pseudosubstrate interaction with the active site of the C subunit (Mochida et al., 2010; Williams et al., 2014). Preliminary structure-function analysis and crosslinking studies indicate that a central region of ENSA containing phospho-Ser67 inhibits PP2A and can bind to both the C and B55 subunit (Mochida, 2014). Studies of ARPP-16 indicate that it can bind to sub-regions of the A subunit in an unphosphorylated state (Andrade et al., 2017). The ARPP/ENSA proteins therefore are likely to be able to interact through a number of different regions with PP2A heterotrimers. Our studies of the PKA-phosphorylated form of ARPP-16 indicates that it could act in a dominant-negative manner and antagonize the inhibitory actions of phospho-S46-ARPP-16 on PP2A. Perhaps the phospho-S88/S88D forms of ARPP-16 bind favourably to the A or B subunits in a way that competes with the phospho-Ser46 form. The ability of the PKA-phosphorylated ARPP-16 to act as a dominant negative might also provide part of the explanation for the studies carried out in intact Xenopus oocytes which showed that increasing amounts of phospho-S109-ARPP-19 resulting from endogenous PKA activity, or introduction of an Ser109 to aspartate phospho-mimetic form of ARPP-19, blocked the effect of phospho-S67-ARPP-19 on progesterone-induced meiotic maturation (Dupré et al., 2014).

Bistability and switch-like behaviour have been considered as basic building blocks in molecular biology and explored both computationally and experimentally (Gardner et al., 2000; Kellershohn and Laurent, 2001; Cross et al., 2002). The discontinuous, two-way switch is often referred to as a toggle switch, in which a system can be switched on or off based on quantitatively different signal thresholds, therefore preventing premature activation or inactivation (Tyson et al., 2003). The ARPP/ENSA/Gwl system has been shown to produce a toggle switch response to Cdk1 activity, based on the antagonism between Cdk1 and PP2A (Mochida et al., 2010; Vinod and Novak, 2015). However, there is so far no model that explores the antagonistic effect between the two phosphorylation sites on ENSA/ARPP. To model PP2A inhibition by ARPP-16, we adapted the mechanism proposed by Vinod and Novak (2015) for the inhibition by ENSA. In addition, we focused on the reciprocal relationship between phospho-Ser46 and phospho-Ser88 mediated by inhibition of PKA and MAST3, and demonstrated that ARPP-16 is capable of switching its phosphorylation states depending on different cAMP concentration thresholds, therefore dynamically controlling PP2A activity. The additional layers of PKA regulation, including directly inhibiting MAST3 and dominant negative antagonism of PP2A inhibition, reduce and almost equalize the two cAMP thresholds for switching on or off PP2A inhibition. These regulations together produce an ultra-sensitive system in response to cAMP at micromolar-range concentrations and deepen the release of PP2A inhibition. The micromolar-range cAMP concentrations required for bi-stable states provide sensitive thresholds in most subcellular neuronal compartments, given the fast diffusion coefficient of cAMP (Hempel et al., 1996; Chen et al., 1999), and the likely very high activity of cAMP phosphodiesterase activity in striatal neurons (Sharma et al., 2015). However, the network we studied here is relatively small, and the antagonistic inhibition exerted by ARPP-16's MAST3 phosphorylation site to its PKA site could be strengthened by the feedback loop from PP2A inhibition to PKA inhibition, via for example, phospho-Thr75-DARPP-32 (Bibb et al., 1999). As shown in the two-parameter bifurcation diagrams (Figure 3—figure supplements 6 and 7), the stronger phospho-Ser46 antagonizes Ser88 phosphorylation, the wider the difference between two cAMP thresholds becomes, therefore the more hysteresis appears. Future modeling work will be useful to understand the complex interrelationship between the abundant PKA substrates, ARPP-16/ENSA, DARPP-32 and ARPP-21/RCS, all of which play important roles in striatal neurons (Walaas et al., 2011).

Through in vitro analysis, cell-based studies and molecular modeling, the current study supports a complex inter-relationship between the functional effects of the MAST3 and PKA sites in ARPP-16, and the presence of additional regulatory mechanisms that couple changes in cAMP levels to generate a switch-like control over PP2A activity. Elements of the switch-like process are summarized in the two schematic illustrations shown in Figure 8. Phosphorylation of ARPP-16 at Ser88 by PKA suppresses the phosphorylation of Ser46 phosphorylation by MAST3, while conversely phosphorylation of Ser46 by MAST3 acts in a reciprocal manner to suppress phosphorylation of Ser88 by PKA (Figure 8a). In turn, the balance of MAST3 kinase and PKA activities determines the state of phosphorylation of Ser46 and Ser88. For example, under basal conditions in neurons in striatum, MAST3 activity is high and PKA activity is low, resulting in high levels of phospho-Ser46 and inhibition of specific heterotrimeric forms of PP2A (Figure 8b, left panel). Following its activation by cAMP, PKA acts at three different levels to prevent the inhibition of PP2A isoforms. Phosphorylation of Ser88 suppresses the ability of MAST3 to phosphorylate Ser46. PKA also phosphorylates MAST3 at multiple sites to inhibit its activity (Figure 8b, upper right panel). Finally, phospho-Ser88-ARPP-16 acts in a dominant-negative manner to prevent inhibition of PP2A by ARPP-16 phosphorylated at Ser46 (Figure 8b, lower right panel). The complex interplay likely serves to underlie the balance of signaling pathways that act via ARPP-16 and PP2A in distinct ways in neurons and other cell types. While the current study and the work of others has elucidated many details of the regulation of PP2A by ARPP-16/19/ENSA, several important questions remain. The identity of the phosphatase(s) that dephosphorylate ARPP-16/19/ENSA at the PKA site, as well as of phosphatases that dephosphorylate various sites in MAST3, are not known. Regulation of these and other phosphatases would potentially add further complexity to this signaling hub. Additional structural analyses will also be needed to elucidate the molecular details related to how GwL/MAST-phosphorylated ARPP-16/19/ENSA inhibit PP2A.

Materials and methods

Materials

pET28-ARPP-16, pET28-S46D-ARPP-16, pET28-S88D-ARPP-16, and pCMV-HA-ARPP16, pCMV-HA-S88D-ARPP16, pCMV-HA-S46D-ARPP16, pCMV-HA-MAST3 (sequence corresponding from aa313 to aa1021 of the human protein, all the other mutant constructs are variation of the same sequence, aa1-1021), pCMV-HA-MAST3-FL (human MAST3 full length sequence), pCMV-HA-T389D-MAST3, pCMV-HA-T389A-MAST3, pCMV-HA-S512D-MAST3, pCMV-HA-S512A-MAST3, pCMV-HA-T628A-MAST3, pCMV-HA-T628D-MAST3, pCMV-HA-S747A-MAST3 and CMV-HA-S747D-MAST3 were prepared using standard procedures (see below). Antibodies used for these studies include: anti-rabbit total ARPP-16 (1:1000 G153, [Horiuchi et al., 1990]), anti-rabbit P-S46-ARPP-16 (1:1000 RU1102, [Andrade et al., 2017]), anti-rabbit P-S88-ARPP-16 (1:500 G446 [Dulubova et al., 2001]), anti-HA antibody (catalog #05–904 RRID:AB_11213751; 1:1000 Cell Signaling Technology, Inc), anti-HA antibody (Novus Biological) used for immunoprecipitation, anti-FLAG (catalog #F1804 RRID:AB_262044; Sigma-Aldrich), anti-GAPDH antibody (catalog CB1001-500UG RRID:AB_2107426; 1:3000, EMD Millipore, anti-MAST3 antibody (catalog #BS5790, AB_2661882; 1:1000, Bioworld Technology, Inc), anti-total GluR1 antibody (catalog #04–855 RRID:AB_1977216; 1:1000, EMD Millipore), anti-pS845 on GluR1 antibody (catalog #p1160-845 RRID:AB_2492128; 1:1000, PhosphoSolutions), monoclonal total DARPP-32 (1:5000 6a [Ouimet et al., 1984]), anti-rabbit pT34 on DARPP-32 (1:1000 cc500 [Stipanovich et al., 2008]). Recombinant PKA was from Millipore). Forskolin and Okadaic Acid were from Abcam. R(+)-SKF-81297 hydrobromide (catalog S179-5MG) was from Sigma-Aldrich.

Site-directed mutagenesis of ARPP-16 and MAST3

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Mutations of ARPP-16 Ser46 and Ser88 and of each potential PKA phosphorylation site for MAST3 were performed using the QuikChange XL site-directed mutagenesis kit according to the manufacturer's instructions (Stratagene). Briefly, 50 ng of pET28-ARPP-16, pCMV-HA ARPP-16, pCMV-HA MAST3 were used as a template with the following mutant nucleotide primers: for ARPP-16, S46A forward primer 5’-tgcagaaagggcaaaagtattttgatgctggggattacaac-3’, S46A reverse primer 5’- gttgtaatccccagcatcaaaatacttttgccctttctgca-3’, S46D forward primer 5’- aaaagattgcagaaagggcaaaagtattttgatgatggggattacaacatgg-3’, S46D reverse primer 5’- ccatgttgtaatccccatcatcaaaatacttttgccctttctgcaatctttt-3’, S88A forward primer 5’- cctccctcagcggaaaccagccctggttgc-3’, S88A reverse primer 5’- gcaaccagggctggtttccgctgagggagg-3’, S88D forward primer 5’- ctccctcagcggaaaccagacctggttgctagc-3’, S88D reverse primer 5’- gctagcaaccaggtctggtttccgctgagggag-3’; for MAST3, T389A forward primer 5’-gcggcaccgtgacgcccggcagcgctttg-3’, T389A reverse primer 5’-caaagcgctgccgggcgtcacggtgccgc-3’, T389D forward primer 5’-gtgcggcaccgtgacgaccggcagcgctttgcc-3’, T389D reverse primer 5’-ggcaaagcgctgccggtcgtcacggtgccgcac-3’, S512A forward primer 5'-ggacttcggcctggccaagatcggcct-3', S512A reverse primer 5'-aggccgatcttggccaggccgaagtcc-3', S512D forward primer 5'-cacggacttcggcctggacaagatcggcctcatg-3', S512D reverse primer 5'-catgaggccgatcttgtccaggccgaagtccgtg-3', T628A forward primer 5'-accgtctgggcgctggtggcaccc-3', T68A reverse primer 5'-gggtgccaccagcgcccagacggt-3', T628D forward primer 5'-ggaccgtctgggcgatggtggcacccac-3', T628D reverse primer 5'-gtgggtgccaccatcgcccagacggtcc-3', S747A forward primer 5'-tggccgccggctggctgctgacatccgg-3', S747A reverse primer 5'-ccggatgtcagcagccagccggcggcca-3', S747D forward primer 5'-tggccgccggctggatgctgacatccgg-3', S747D reverse primer 5'-ccggatgtcagcatccagccggcggcca-3'.

DNA sequencing identified appropriate clones.

Expression of recombinant proteins

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6xHis-ARPP-16, 6xHis-S46D-ARPP-16 and 6xHis-S88D-ARPP-16 proteins were overexpressed in BL21 (DE3) cells, lysed using sonication in 300 mM NaCl, 50 mM potassium phosphate, 1% Triton X-100 pH 8.0 and immobilized on Profinity IMAC Ni2+ charged resin (Bio-Rad, Hercules, CA). The resin was washed 3 times in 300 mM NaCl, 50 mM potassium phosphate, 10 mM imidazole, pH 8.0. Proteins were eluted using a buffer containing 300 mM NaCl, 50 mM potassium phosphate, 250 mM imidazole, pH 8.0. Buffer exchange (150 mM NaCl, 25 mM TrisHCl pH 7.4), used PD10 desalting columns (GE Healthcare Life Science), 1 ml fractions were collected, and protein content was determined using SDS-PAGE.

HEK293T protein expression

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293T/17 [HEK 293T/17] (ATCC CRL11268) were purchased from AMERICAN TYPE CULTURE COLLECTION (ATCC) (lot number 58483269, cell species identity has been confirmed by COI assay (intraspecies) and STR assay (interspecies); cells tested negative for mycoplasma). Cells were cultured to 60–70% confluence in 10% FBS-DMEM. Expression plasmids (HA-ARPP-16, HA-S46D-ARPP-16, HA-S88D-ARPP-16, HA-MAST3 [aa from 332 to 1014], HA-T389D-MAST3, HA-T389A-MAST3, HA-S512D-MAST3, HA-S512A-MAST3, HA-T628A-MAST3, HA-T628D-MAST3 and HA-S747A-MAST3 were expressed in HEK293T cells in 10 cm plates using Lipofectamine 2000 transfection (Invitrogen). Immunoblotting for ARPP-16 or MAST3 was routinely used to control for levels of protein expression. Expression levels of ARPP-16 plus endogenous ARPP-19, or MAST3, were approximately equal to that of ARPP-16 plus ENSA, or MAST3, in adult striatum. Twenty-four hours after transfection, cells were lysed in a buffer containing 25 mM Tris, pH 8.0, 150 mM NaCl, 0.1% Triton X-100, protease and phosphatase inhibitors cocktails, followed by centrifugation at 16,000 x g, 10 min. Supernatants were subjected to immunoprecipitation or immunoblotting as described below.

Striatal slice preparation

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Male C57BL/6 mice at 6–8 weeks old were purchased from Charles River. All mice used in this study were handled in accordance with the Yale University Animal Care and Use Committee (IACUC) and NIH Guide for the Care and Use of Laboratory Animals. Male C57BL/6 mice were sacrificed by decapitation. The brains were rapidly removed and placed in ice-cold, oxygenated Krebs-HCO3- buffer (124 mM NaCl, 4 mM KCl, 26 mM NaHCO3, 1.5 mM CaCl2, 1.25 mM KH2PO4, 1.5 mM MgSO4 and 10 mM D-glucose, pH 7.4). Coronal slices (350 μm) were prepared using a vibrating blade microtome (VT1000S, Leica Microsystems, Nussloch, Germany), as described previously (Nishi et al., 2005). From each mouse, six striatal slices were dissected from the coronal slices in ice-cold Krebs-HCO3 buffer. Each slice was placed in a polypropylene incubation tube with 2 ml fresh Krebs-HCO3- buffer containing adenosine deaminase (10 μg/ml). The slices were preincubated at 30˚C under constant oxygenation with 95% O2/5% CO2 for 60 min. The buffer was replaced with fresh Krebs-HCO3- buffer after 30 min of preincubation. Each slice was treated with drug as specified in each experiment. After drug treatment, slices were transferred to Eppendorf tubes, frozen on dry ice, and stored at –80˚C until assayed. Frozen tissue samples were sonicated in boiling 1% sodium dodecyl sulfate (SDS) and boiled for an additional 10 min. Total lysate (equal volume for each sample) was analyzed by SDS-PAGE and immunoblotting as described below.

Immunoblotting

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Cell lysates were separated by SDS-PAGE (Novex 10–20% or 4–20% gradient gels (Invitrogen)) and transferred onto nitrocellulose membranes, 0.2 μm (Bio-Rad). The membranes were blocked for 1 hr at room temperature (5% nonfat dry milk in PBS, 0.1% Tween 20) and immunoblotted overnight at 4°C, with specific antibodies as indicated. Antibody binding was detected using IRDye800-conjugated anti-mouse IgG (catalog #610-102-041 RRID:AB_2614830; 1:10.000; Rockland) or IRDye680-conjugated anti-rabbit IgG (catalog #926–68021 RRID:AB_10706309; 1:10.000 LI-COR, Bioscience). Blots were analyzed and quantified using an Odyssey Infrared Imaging System (LI-COR Bioscience, Image Studio Lite version 3.1, RRID:SCR_013715). Anti-rabbit pS88 (G446), and anti-rabbit pS46 (RU1102) for ARPP-16 were detected using peroxidase-conjugated secondary antibody (catalog #PI-1000 RRID:AB_2336198; 1:300, Vector Laboratories,Inc.) coupled with a chemiluminescence detection system (Pierce, ThermoScientific).

ARPP-16 in vitro phosphorylation

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For Ser46 phosphorylation, purified 6xHis-ARPP-16 proteins (100 μM) were incubated with immunoprecipitated MAST3 kinase in the presence of 200 μM ATP or thio-ATP (Sigma) in phosphorylation assay buffer (50 mM Hepes, pH 7.4, 10 mM MgCl2) at 30°C for different times. For Ser88 phosphorylation, purified 6xHis-ARPP-16 proteins (100 μM) were incubated with recombinant PKA in the presence of 200 μM ATP or thio-ATP in phosphorylation assay buffer (50 mM Hepes, pH 7.4, 10 mM MgCl2, 1 mM EGTA) at 30°C for different times. Phospho-ARPP-16 preparations were isolated (where needed) by ion-exchange chromatography using an FPLC.

Immunoprecipitation and phosphatase assay

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PP2A-Bα subunit was expressed in HEK293T cells and cells lysed as described above. Lysates were incubated with 50 μl (50% slurry) of anti-FLAG conjugated agarose beads for 2 hr at 4°C. Immunocomplexes were washed 3 times in lysis buffer without phosphatase inhibitors and 2 times in PP2A reaction buffer (pNPP Ser/Thr Assay Buffer, Phosphatase Assay Kit, Millipore). The PP2A-Bα trimer immunocomplex was resuspended in 100 μl of PP2A reaction buffer and incubated without or with thio-phosphorylated P-S46-ARPP-16, or P-S46-ARPP-16 plus: ARPP-16, or P-S88-ARPP-16, or S88D-ARPP-16 (200 nM each) for 10 min at 37°C in the presence of 500 μM phosphopeptide (K-R-pT-I-R-R). Phosphatase activity was measured using a malachite green assay kit (Millipore).

MAST3 immunoprecipitation and phosphorylation

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MAST3-HA, T389A-MAST3-HA, or T389A-MAST3-HA were expressed in HEK293 and cells lysed as described above. Lysates were incubated with 50 μl (50% slurry) of anti-HA conjugated agarose beads for 2 hr at 4°C. Immunocomplexes were washed 3 times in RIPA buffer and 3 times in a buffer without detergents (150 mM MgCl2, 25 mM Tris-HCl pH 7.4) and resuspended in PKA reaction buffer (50 mM Hepes, pH 7.4, 10 mM MgCl2, 1 mM EGTA). The immunoprecipitated kinases were incubated with PKA in the presence of 200 μM [γ−32P] ATP at 30°C for different times. In the experiments that examined the effect of PKA-dependent phosphorylation on MAST3 activity, MAST3 immunocomplexes were incubated without or with PKA for 30 min together with ATP, washed twice with kinase buffer, then incubated with ARPP-16 as described above.

Quantification and statistical analysis

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Immunoblots and 32P autoradiograms were analyzed by Fiji ImageJ (http://fiji.sc/wiki/index.php/Fiji) or Image Studio (Licor Bioscience, Lincoln, NE, USA). Graphical presentations and statistical analyses were made using Graph Pad Prism (RRID:SCR_002798; Graph Pad Software).

Mass spectrometry

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Samples were processed in three different ways. (1) In initial studies, phosphopeptides were enriched and subsequently analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis using a LTQ Orbitrap Elite instrument. Label-free quantitation was carried out retrospectively using Progenesis QI software; (2) Information Dependent Acquisition (IDA) and SWATH acquisitions were carried using an AB SCIEX Triple TOFTM 5600 instrument; (3) In later experiments where phosphopeptides were not enriched, samples were analyzed using a Q-Exactive Plus. Label-free quantitation was carried out using Progenesis QI software.

(i) Sample preparation: HEK293T cells were transiently transfected with HA-tagged wild-type MAST3 using Lipofectamine 2000 for 24 hr and then either mock treated with DMSO or treated with 10 μM FSK for 30 min. The cells were lysed and MAST3-HA was immunoprecipitated as described above. For (1) and (3), immunoprecipitated MAST3-HA was subjected to SDS-PAGE, and Coomassie stained gel bands corresponding to the MAST3-HA protein were in-gel digested. For (2), samples analysed by SWATH were not subjected to SDS-PAGE. Samples were digested with Lys C, chymotrypsin, LysC/trypsin, or chymotrypsin/trypsin in attempts to maximize coverage of the protein (see Supplementary file 1). Digestions were carried out at 1:10 ratio of enzyme:protein at 37°C for 16 hr. In the later experiments (3), samples were digested with trypsin (MS grade Promega; incubation at 37°C overnight), peptides were extracted utilizing an 80% acetonitrile solution containing 0.1% formic acid, and dried. Dried extracted peptides were reconstituted in Buffer A (100% water, 0.1% formic acid, and analyzed).

(ii) Phosphopeptide enrichment: Phosphopeptides present in initial digested samples were enriched using an in-house titanium oxide (TiO2) enrichment method with Glycen TopTips (Glycen Corporation, Columbia, MD [according to the manufacturer’s manual]. Briefly, the samples were acidified with 0.5% trifluoroacetic acid (TFA)–50% acetonitrile and loaded onto TopTips (Glygen Corp.), followed by washing three times with 100% acetonitrile, 0.2 M sodium phosphate buffer (pH 7.0), 0.5% TFA, and then 50% acetonitrile. Phosphopeptides were eluted from the TopTip using 28% ammonium hydroxide, dried in a SpeedVac, and then redried from water. Samples were resuspended in 70% formic acid and then immediately diluted to 0.1% TFA for mass spectrometry analysis. Both the flow-through (FT) from the washes and the eluted enriched (EN) fractions were analyzed by high resolution LC-MS/MS on a LTQ-Orbitrap MS system. Quantitation of phosphopeptide levels was carried out retrospectively using Progenesis QI software.

(iii) Mass spectrometry and data analysis: (1) Samples were analyzed by LC-MS/MS using an LTQ Orbitrap Elite equipped with a Waters nanoACQUITY ultra-performance liquid chromatography (UPLC) system using a Waters Symmetry C18180 μm x 20 mm trap column and a 1.7 μm (75 μm inner-diameter x 250 mm) nanoACQUITY UPLC column (35°C) for peptide separation. Trapping was done at 15 μl/min with 99% buffer A (100% water, 0.1% formic acid) for 1 min. Peptide separation was performed at 300 nl/min with buffer A and buffer B (100% CH3CN containing 0.1% formic acid). A linear gradient (51 min) was run with 5% buffer B at initial conditions, 50% buffer B at 50 min, and 85% buffer B at 51 min, and re-equilibration was carried out for 20 min. Blank injections runs were implemented in between sample injections to ensure no carryover. Mass spectral data were acquired in the Orbitrap using 1 microscan and a maximum inject time of 900 μs followed by data-dependent MS/MS acquisitions in the ion trap (via collision-induced dissociation [CID]) and in the high-energy collision dissociation (HCD) cell. Neutral loss scans were also obtained for 98.0, 49.0, and 32.7 atomic mass units (amu). The data were analyzed using Mascot Distiller and the Mascot search algorithm.

 (2) Information Dependent Acquisition (IDA) and SWATH acquisitions were carried out on an AB SCIEX Triple TOFTM 5600 coupled with a Waters nanoACQUITY UPLC system. Digests were loaded onto a Waters Symmetry C18180 µm x 20 mm trap column and separated on a nanoACQUITY 1.7 μm BEH300 C18 (75 µm x 150 mm) column. Peptide separations were performed at 500 nl/min with Buffer A (0.1% formic acid) and Buffer B (CH3CN containing 0.1% formic acid). A linear gradient (70 min) was run with 1% buffer B at initial conditions, 35% buffer B at 70 min, 95% buffer B at 70.33 min, and column re-equilibration for 20 min. IDA runs were carried out at high sensitivity with resolution ~16–18K in the MS/MS (w/max of 30 MS/MS per cycle) at 0.05 s/scan and TOFMS scan of 0.25 s. SWATH acquisition was carried out over the mass range of 400–1250 with setting at 26 Da scan window with 1 Da overlap (i.e. 400–425, 424–450, etc.) for a total of 34 SWATH windows per cycle. Acquired LC MS/MS data were analyzed using Analyst (v.2.5) and PeakView (v.2.0, AB SCIEX). Protein identification and site modification assignment utilized MASCOT (Matrix Science) searches; modification sites of interest were manually verified. Skyline (MacLean et al., 2010) was utilized to obtain precursor ion quantitation from SWATH data on the various modification sites.

(3) Samples were isolated from HEK293T cells essentially as described above. Four control and four forskolin-treated samples were analyzed, each as a technical duplicate. Samples were analysed using a Q-Exactive Plus (Thermo Fisher Scientific) LC MS/MS system equipped with a Waters nanoAcquity UPLC system, that used a Waters Symmetry C18180 µm x 20 mm trap column and a 1.7 µm (75 µm x 250 mm) nanoACQUITY UPLC column (37°C) for peptide separation. Trapping was done at 5 µl/min, 99% Buffer A for 3 min. Peptide separation was performed with a linear gradient over 140 min at a flow rate of 300 nL/min. Precursor mass scans (300 to 1500 m/z range, target value 3E6, maximum ion injection times 45 ms) were acquired and followed by HCD based fragmentation (normalized collision energy 28). A resolution of 70,000 at m/z 200 was used for MS1 scans, and up to 20 dynamically chosen, most abundant, precursor ions were fragmented (isolation window 1.7 m/z). The tandem MS/MS scans were acquired at a resolution of 17,500 at m/z 200 (target value 1E5, maximum ion injection times 100 ms).

Relative changes in phosphopeptide levels were calculated based on the detected abundances of the precursor mass utilizing Progenesis QI (v. 3.0). Raw mass spectral data were first aligned based on their retention time, then peaks are picked based on a ‘co-detection’ scheme where ion detection was performed once on a single aggregate run, and multiple isotopic forms of the same ion were grouped to provide the abundance of that ion (the precursor mass) across the various runs. The protein level quantitation of MAST3 was then normalized (based on total abundance method) across all runs to correct for experimental (4 biological replicates) or technical (2 technical replicates) variations. The normalization was then attributed to the quantitation of the precursor mass corresponding phosphopeptide identified in previous runs. The differential analysis used to generate the Anova p value takes into account the mean difference and the variance and also the sample size. Thus small differences with small variance were considered significant (hence low p-values).

Computational modelling

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Mathematical models were written to describe the mutually antagonistic effect of Ser46 and Ser88 phosphorylation on PKA and MAST3, respectively, as well as the direct inhibition from PKA to MAST3, and the ‘dominant-negative’ role of P-S88-ARPP-16 on PP2A inhibition. In these models, upon phosphorylation at Ser46 by MAST3, ARPP-16 becomes a stoichiometric inhibitor with high affinity binding, as well as being a substrate of PP2A. This results in low catalytic efficiency of PP2A. We hypothesized that P-S46-ARPP-16 inhibits PKA activity and lowers PKA catalytic efficiency, whereas P-S88-ARPP-16 inhibits MAST3 and weakens its catalytic efficiency as well. Our preliminary experimental results indicate that phospho-Ser88 is not dephosphorylated by PP2A, and for the model we assumed that dephosphorylation at Ser88 was catalyzed by PP1. For modeling the direct inhibition from PKA to MAST3, we assumed that PKA not only inactivates MAST3, but inactivated MAST3 also interferes with active MAST3 phosphorylation of ARPP-16. Finally, we hypothesized that P-S88-ARPP-16 antagonizes PP2A inhibition by weakening the binding between P-S46-ARPP-16 and PP2A.

All phosphorylation and dephosphorylation reactions were modelled following Michaelis-Menten kinetics (see additional details in Appendix 1). The activation of PKA followed the Hill equation and the parameters were validated against published experimental data (Zawadzki and Taylor, 2004) (see Appendix 1—figure 7). Other regulations were modelled following laws of mass action. Inhibition of PP2A by P-S46-ARPP-16 and dephosphorylation of P-S46-ARPP-16 was modelled as described (Vinod and Novak, 2015). Parameters for PP1 were as described (Hayer and Bhalla, 2005). The total concentrations of each protein were estimated to correspond to their relative expression levels in striatum and were calculated relative to DARPP-32 abundance based on a recent mouse brain proteomic study (Sharma et al., 2015) (see Appendix 1—table 2). We derived the values of the kinetic constant Km for Ser46 and Ser88 phosphorylation based on double reciprocal plots of data from Figure 1b and d. Kinetic constants (kcatPKA and kcatMAST3) and inhibitor constants (k88, k46, a and b) were estimated using the Particle Swam method implemented in the software COPASI (Hoops et al., 2006) and based on the data presented in Figure 1a-d (see Appendix 1—the mutual inhibition model and Table 1). Parameters for PKA inactivation of MAST3 (kPKA) and how inactivated MAST3 interferes with catalytic efficiency of active MAST3 (r) were estimated as above, based on data presented in Figure 4b (see Appendix 1—the mutual inhibition plus PKA inhibits MAST3 model and Table 1). The parameter representing how P-S88-ARPP-16 antagonizing PP2A binding to P-S46-ARPP-16 (v) was estimated and validated by comparing simulation results with experimental data (see Appendix 1—the mutual inhibition plus PKA inibits MAST3 and dominant negative model and Table 1). Parameter estimation was performed using the SBPIPE package (Dalle Pezze and Le Novère, 2017). The optimum estimation results from five hundred trials were displayed for every possible pair of parameters under the 95% confidence interval of the best values (see Appendix 1—the first two models). The local minima reached in these estimations indicate that these parameters are identifiable for the given experimental data. Model equations and parameters are listed in Appendix 1. Bifurcation analysis was conducted with XPP-Aut (Ermentrout, 2002). The models are available in the BioModels Database (Juty et al., 2015)(MODEL1707020000, MODEL1707020001, MODEL1707020002).

Appendix 1

Description of the mathematical models

Mutual inhibition between P-S46-ARPP-16 and P-S88-ARPP-16

Appendix 1—scheme 1
Equations
KmKd=(A46A46:PP2A)(PP2AtotA46:PP2A)A46:PP2A
A46:PP2A=12(A46+PP2Atot+Km(A46+PP2Atot+Km)24A46PP2Atot)
dA46dt=kcatMAST3MAST3(AtotA46)KmMAST3+aA88Atot+(AtotA46)kcatPP2AA46:PP2A
dA88dt=kcatPKAPKA(AtotA88)KmPKA+bA46Atot+(AtotA88)kcatPP1PP1A88KmPP1+A88
dMAST3dt=kPPX(MAST3totMAST3)k88A88MAST3
dPKAdt=kcAMP(PKAtotPKA)cAMPnKAn+cAMPnk46A46PKA

A46 concentration of P-S46-ARPP-16

A46:PP2A concentration of the complex between P-S46-ARPP-16 and PP2A

A88 concentration of P-S88-ARPP-16

Atot total concentration of ARPP-16

Distributions of estimated parameters converge towards optimal values.
Appendix 1—figure 1
Appendix 1—figure 2
Parameter estimation results.
Appendix 1—figure 3

Mutual inhibition + PKA's direct regulation on MAST3

Appendix 1—scheme 2
Equations
KmKd=(A46A46:PP2A)(PP2AtotA46:PP2A)A46:PP2A
A46:PP2A=12(A46+PP2Atot+Km(A46+PP2Atot+Km)24A46PP2Atot)
dA46dt=kcatMAST3MAST3(AtotA46)KmMAST3+aA88Atot+r(MAST3totMAST3)MAST3tot+(AtotA46)kcatPP2AA46:PP2A
dA88dt=kcatPKAPKA(AtotA88)KmPKA+bA46Atot+(AtotA88)kcatPP1PP1A88KmPP1+A88
dMAST3dt=kPPX(MAST3totMAST3)k88A88MAST3kPKAPKAMAST3
dPKAdt=kcAMP(PKAtotPKA)cAMPnKAn+cAMPnk46A46PKA
Distributions of estimated parameters converge at the best values.
Appendix 1—figure 4
Parameter estimation results.
Appendix 1—figure 5

Mutual inhibition + PKA inhibits MAST3 + P-S88-ARPP-16 dominant negative effect

Appendix 1—scheme 3
Equations

When only mutual inhibition and P-S88-ARPP-16 dominant negative effect were considered, the red parts of the equations were not included.

KmKd=(A46A46:PP2A)(PP2AtotA46:PP2A)A46:PP2A
A46:PP2A=A46+PP2Atot+Km(1+ vA88Atot)(A46+PP2Atot+Km(1+vA88Atot))24A46PP2Atot2
dA46dt=kcatMAST3MAST3(AtotA46)KmMAST3+aA88Atot+r(MAST3totMAST3)MAST3tot+(AtotA46)kcatPP2AA46:PP2A
dA88dt=kcatPKAPKA(AtotA88)KmPKA+bA46Atot+(AtotA88)kcatPP1PP1A88KmPP1+A88
dMAST3dt=kPPX(MAST3totMAST3)k88A88MAST3kPKAPKAMAST3
dPKAdt=kcAMP(PKAtotPKA)cAMPnKAn+cAMPnk46A46PKA
Parameter estimation results.
Appendix 1—figure 6
Appendix 1—figure 7
Appendix 1—table 1
Appendix 1—table 2

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Decision letter

  1. Roger J Davis
    Reviewing Editor; University of Massachusetts Medical School, United States

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Regulation of ARPP-16 by PKA and MAST3 kinases provides a cAMP-regulated switch in phosphatase PP2A inhibition" for consideration by eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Jonathan Cooper as the Senior Editor. The following individual involved in review of your submission has agreed to reveal his identity: John D Scott (Reviewer #2).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission..

Summary:

This is an interesting manuscript that describes a study that was designed to examine the interplay between MAST3 and PKA signaling in the regulation of PP2A by ARRP-16.

Previous studies have established that ARPP-19 is phosphorylated and inhibited by PKA (Dupre et al., 2014) and that PKA phosphorylation of ARPP-16/19 on Ser88 is associated with dephosphorylation of ARPP-16/19 phosphorylation on the MAST3 site Ser46 that mediates PP2A inhibition (Andrade et al., in revision).

The present study seeks to identify mechanisms that mediate the inhibitory actions of PKA on MAST3-regulated PP2A activity. Three different mechanisms are described. First, ARPP-16 phosphorylated more slowly by PKA and MAST3, if either Ser46 or Ser88, respectively, is already phosphorylated. This is an elegant mechanism of cross-regulation, presumably induced by changes in substrate conformation. Second, PKA phosphorylates and inhibits MAST3. Third, phosphorylation of ARPP-16 at Ser88 can inhibit the effects of phosphoSer46 ARPP-16 on PP2A inhibition by a dominant-negative mechanism in trans. Collectively, these data indicate that there are multiple levels of cross-regulation of MAST3 signaling to ARPP-16 by PKA.

Essential revisions:

1) While three different forms of cross-regulation by PKA are identified, this is not apparent in the abstract of the manuscript. Moreover, the computational model presented does not capture this complexity. Furthermore, this complexity is not illustrated in the final cartoon model presented in Figure 8.

2) The Supplementary Materials support the mathematical model written and employed, with respect to adequate fitting of time-course data and cAMP-dependence of PKA activity. The model itself appears to be satisfactory under various assumptions essentially setting other cell biochemistry processes irrelevant. The steady-state bifurcation analysis described is appropriate. However, as noted above, the model does not take account of MAST3 inhibition by PKA or the dominant-negative effects of ARPP-16 phosphorylation. The importance of the model comes from predictions derived from the model that can be experimentally tested. For example, what happens if one tests the hypothesis upon stimulation with isoproterenol, dopamine or epinephrine?. Or inhibits PKA with H89 or PKI peptides? Also, are the results skewed upon deletion or silencing MAST3. This latter experiment is important to establish if other kinases compliment for MAST3 activity inside cells.

3) The biochemical studies in Figure 1 are well performed and provide the first evidence for the model proposed by the authors. However, the next phase of the analyses uses overexpression of proteins in HEK293 cells. While this is a slightly more physiologically significant system it would be prudent to include data showing the phenomena in primary cultures of neurons.

4) Another weakness of the experiments in Figure 2 is that forskolin (a direct activator of Adenylyl cyclases) is used as a means to over stimulate cAMP synthesis. This creates a situation where cells accumulate cAMP at levels that are 15-20 fold above the normal physiological range. Can experiments be done in HEK293 cells with more natural agonists such as isoproterenol, dopamine or epinephrine.

5) The cross-inhibitory effects of Ser46 and Ser88 phosphorylation on subsequent ARPP-16 phosphorylation and PP2A inhibition are interesting. These observations raise questions concerning whether dual phosphorylated endogenous ARPP-16 is found in vivo. This could be tested by immunoprecipitation/western blot analysis using phospho-specific antibodies.

6) PKA phosphorylates and inhibits MAST3 in in vitro (Figure 4). Does cAMP signaling in vivo inhibit endogenous MAST3 activity?

7) PKA appears to inhibit MAST3 activity in HEK transfection assays using ARPP-16 Ser46 as a read-out (Figure 5 and 6). Studies using phosphorylation site mutations are unclear. Presumably, if the phosphorylation sites were important for MAST3 activity, there should be major differences MAST3 activity between the WT and Ser/Ala mutants in the presence of activated PKA signaling. However, the data presented shows that the WT and mutated MAST3 proteins cause similar Ser46 ARPP-16 phosphorylation in each case. Please clarify.

8) The ability of Ser88 phosphorylated ARPP-16 to act as a dominant inhibitor of Ser64 phosphorylated ARPP-16 is intriguing. This suggests that the two ARPP-16 proteins might compete for binding to PP2A and that pSer88 ARP-16 has a higher affinity for PP2A than pSer64 ARPP-16. Alternatively, the binding may be non-competitive and pSer88 ARPP-16 may have dominant activity. Other mechanisms are also possible. Studies to evaluate mechanism are required, including the determination of binding affinity and binding competition.

https://doi.org/10.7554/eLife.24998.034

Author response

Essential revisions:

1) While three different forms of cross-regulation by PKA are identified, this is not apparent in the abstract of the manuscript. Moreover, the computational model presented does not capture this complexity. Furthermore, this complexity is not illustrated in the final cartoon model presented in Figure 8.

Abstract – The Abstract has been edited to describe more fully the content of the manuscript. Note that due to the 150-word limit numerous edits/deletions were made to the text.

Computational model – As described in the response to comment #2, we have made significant additions to the computational modeling as suggested by the reviewers.

Figure 8 – The three different forms of cross-regulation of PP2A by PKA were included in the original Figure 8B, but we did not explain this clearly in the legend. The legend has been expanded, and we have also revised the text in the Discussion related to Figure 8B.

2) The Supplementary Materials support the mathematical model written and employed, with respect to adequate fitting of time-course data and cAMP-dependence of PKA activity. The model itself appears to be satisfactory under various assumptions essentially setting other cell biochemistry processes irrelevant. The steady-state bifurcation analysis described is appropriate. However, as noted above, the model does not take account of MAST3 inhibition by PKA or the dominant-negative effects of ARPP-16 phosphorylation.

We modified and re-estimated the parameters in the original mathematical model representing the reciprocal inhibitions (layer 1), using not only the time-course experimental data (Figure 1A and C), but also the dose-response experiments (Figure 1B and D). These changes provide a better match between simulation results from the model and experimental observations, while keeping intact the switch response to cAMP at similar dynamic cAMP concentration ranges (Appendix Section).

We then extended the model to include MAST3 inhibition by PKA (layer 2) and the dominant negative effect of P-S88-ARPP-16 on PP2A inhibition (layer 3). We estimated the corresponding parameters against our own experiments (Appendix Section). Model simulations showed that these additional layers of PKA regulation maintain bistability and switch-like control over PP2A activity. However these regulations shift the cAMP dynamic range for bistability to a more sensitive micro-molar level, and most importantly, deepen the PP2A dis-inhibition when cAMP increases above the threshold (see Figure 3—figure supplement 1 and 2).

We further studied how total MAST3 concentration change affects switch-like control of PP2A activity, as MAST3 or other MAST isoforms are expressed at different levels in different brain regions and potentially could exert similar effects on PP2A. Our simulation results revealed that there is a wide range of total MAST3 concentration that supports bistability and therefore switch-like control over PP2A inhibition (see Figure 3—figure supplement 3, 4, 5).

Finally we briefly discuss the potential impact of the striatal PP1 inhibitor, DARPP-32, that can potentially modify the cAMP dynamic concentration range through its phosphorylation at Thr75 and inhibition of PKA. The antagonistic inhibition from P-S46-ARPP-16 to Ser88 phosphorylation can be strengthened by the negative regulation from Ser46 phosphorylation to PKA activity, via PP2A and Thr75-DARPP-32. Our simulations showed that the bistability and switch like control over PP2A is robust (Figure 3—figure supplement 6 and 7). However the dynamic range of cAMP concentration controlling the on and off PP2A inhibition is potentially wider (more hysteresis) than what we revealed here.

The importance of the model comes from predictions derived from the model that can be experimentally tested. For example, what happens if one tests the hypothesis upon stimulation with isoproterenol, dopamine or epinephrine?. Or inhibits PKA with H89 or PKI peptides? Also, are the results skewed upon deletion or silencing MAST3. This latter experiment is important to establish if other kinases compliment for MAST3 activity inside cells.

This part of Comment #2 overlaps a bit with Comments #3 and #4, and we apologize for any repetition. We fully agree that one utility of the modeling is to test predictions drawn from the model in an experimental system. However, there are practical limits to the experimental systems we have available. Although we did not add detailed information to the original text, we find that the expression levels of the components of the ARPP/MAST3/PKA/PP2A signaling system are high in adult striatum, but the key proteins, ARPP-16/19/ENSA and MAST3 are very low in cultured striatal neurons. ARPP-19 is expressed in Hek cells to ~50% of the level of ARPP-16/19/ENSA in adult striatum (Author response image 1A) but MAST3 is undetectable in Hek cells (Author response image 1B). Trying to manipulate expression of multiple proteins in primary cultures would be challenging. We therefore chose to establish conditions of ARPP-16/19 and MAST3 expression in Hek cells to be comparable to that seen in adult striatum (Author response image 1A and B). This puts constraints on potential experimental manipulations in Hek cells, with exposure to forskolin being the primary variable used to stimulate cAMP from low basal to high experimental levels. The only other alternative approach available is to use acutely prepared striatal slices from adult rodent, an ex vivo model. These have the intrinsic advantage of being a more physiological representation of adult striatum (see below) but are only amenable to pharmacological manipulation.

Author response image 1

Stimulation with isoproterenol, dopamine or epinephrine – Prior studies have indicated that isoproterenol or epinephrine could be used to increase cAMP levels in Hek cells [e.g. (Schmitt and Stork, 2000; Friedman et al., 2002; Shenoy et al., 2006)], but these studies typically require long-term serum deprivation, pretreatment with pertussis toxin, or over-expression of GPCRs. Under the conditions used for our Hek cell studies, we incubated cells with isoproterenol but we could not see any consistent stimulation of phosphorylation of ARPP-16 by PKA. As an alternative, we used striatal slices and showed that ARPP-16 and ARPP-19 were phosphorylated at high basal levels by MAST3. Stimulation with SKF81297 (a D1 receptor agonist), like forskolin, led to rapid increase in S88-ARPP-16 phosphorylation, which was accompanied by decreased phosphorylation of S46. We have added this data to the manuscript as Figure 2—figure supplement 1.

Inhibition of PKA with H89 or PKI peptides – In striatal slices and the Hek cell model, cAMP is basally low, and as a result S88 is low. Inhibition of PKA would not be expected to have any effect.

Deletion or silencing MAST3 – Given the experimental systems available, we were limited on options (within a reasonable time frame) for deletion or silencing MAST3 in striatal neurons, which would require generating a (preferably) cell type-specific striatal knockout mouse or using viral-mediated manipulation in mouse striatum. We were recently aware of structural work on Greatwall kinase, in which a small molecule inhibitor was generated that appeared to inhibit MAST-L (Ocasio et al., 2016). As an alternative approach to address this comment, we obtained some of the Greatwall inhibitor from these investigators, but it had no effect on MAST3 activity in vitro or on S46 phosphorylation in Hek cells or striatal slices. Our future studies will include targeted silencing of MAST-3 in medium spiny neurons, coupled with biochemical and behavioral studies. As noted above, we did however carry out additional modeling analysis, and found that, over a 4-fold range, varying total MAST3 concentration did not affect the general switch-like response to cAMP concentration changes (Figure 3—figure supplement 3, 4 and 5). This information has been added to the Discussion section.

3) The biochemical studies in Figure 1 are well performed and provide the first evidence for the model proposed by the authors. However, the next phase of the analyses uses overexpression of proteins in HEK293 cells. While this is a slightly more physiologically significant system it would be prudent to include data showing the phenomena in primary cultures of neurons.

As noted above, we examined the levels of the key proteins, ARPP-16/19 and MAST3 in primary cultures that are enriched for striatal neurons, but there was no detectable expression of ARPP-16 and only low levels of expression of ENSA compared to that found in adult striatum. Similarly, the expression of MAST3 was very low in cultured striatal neurons compared to adult striatum. The level of ARPP-16 expression is known to be highly regulated during postnatal brain development, with adult brain levels being reached after several postnatal weeks (Girault et al., 1990). As a result, we decided to use a Hek cell system, where we established conditions to be similar to that seen in adult striatum in terms of ARPP-16/19 and MAST3 expression, with the corresponding basal levels of high S46 phosphorylation and low S88 phosphorylation (Author response image 1A and B and figures in manuscript). As also noted above, we acutely prepared striatal slices and carried out some studies that involve pharmacological manipulation. ARPP-16 and ARPP-19 were phosphorylated at high basal levels by MAST3. Stimulation with SKF81297 (a D1 receptor agonist), like forskolin, led to an increase in S88-ARPP-16 phosphorylation, which was accompanied by decreased phosphorylation of S46. We have added this data to the manuscript as Figure 2—figure supplement 1, as part of a reorganized introduction to the second part of the Results section, where we discuss our rationale for using Hek cells.

4) Another weakness of the experiments in Figure 2 is that forskolin (a direct activator of Adenylyl cyclases) is used as a means to over stimulate cAMP synthesis. This creates a situation where cells accumulate cAMP at levels that are 15-20 fold above the normal physiological range. Can experiments be done in HEK293 cells with more natural agonists such as isoproterenol, dopamine or epinephrine.

We recognize the limitations of using forskolin. As noted above, we attempted to use isoprotereonol in Hek cells but were unable to detect reliable increased phosphorylation of S88-ARPP-16. We think this is a result of low expression of the requisite GPCR [see for example Figure 6 in Shenoy et al., 2006]. We considered trying to overexpress a GPCR as done in other studies, but thought that this would not really help address the reviewer’s comment. As an alternative, as noted above, we used striatal slices and showed that stimulation with SKF81297 (a D1 receptor agonist) led to a rapid increase in S88-ARPP-16 phosphorylation, which was accompanied by decreased phosphorylation of S46. This effect was similar to that of forskolin. We have added this data to the manuscript as Figure 2—figure supplement 1.

5) The cross-inhibitory effects of Ser46 and Ser88 phosphorylation on subsequent ARPP-16 phosphorylation and PP2A inhibition are interesting. These observations raise questions concerning whether dual phosphorylated endogenous ARPP-16 is found in vivo. This could be tested by immunoprecipitation/western blot analysis using phospho-specific antibodies.

This is an interesting question and one that we had considered. As suggested by the reviewer, we attempted to use the phospho-S46 and phospho-S88 antibodies for immunoprecipitation, but unfortunately this was not successful. The two antibodies used are affinity purified, polyclonal rabbit antibodies. While motif-based phospho-serine/threonine antibodies have been used for enrichment of short phosphopeptides, and phospho-tyrosine antibodies are commonly used for immunoprecipitation, we do not know why our antibodies do not immunoprecipitate our holo-proteins. Presumably, the solution-based conditions used for immunoprecipitation are sufficiently different than that used for affinity purification with short epitopes, or for immunoblotting.

6) PKA phosphorylates and inhibits MAST3 in in vitro (Figure 4). Does cAMP signaling in vivo inhibit endogenous MAST3 activity?

Such in vivo studies would require using rodents, and assessing S46-ARPP-16 phosphorylation in striatum as a proxy for MAST3 activity. We have carried out preliminary studies of this type using the psychostimulant, cocaine, to stimulate dopamine receptors and cAMP levels in striatum. The preliminary results showed that acute exposure to cocaine reduced pS46. As an alternative, as noted above, we use here an ex vivo preparation, namely striatal slices, to show that stimulation with SKF81297 (a D1 receptor agonist) led to a rapid decrease in phosphorylation of S46. We have added this data to the manuscript as Figure 2—figure supplement 1.

7) PKA appears to inhibit MAST3 activity in HEK transfection assays using ARPP-16 Ser46 as a read-out (Figure 5 and 6). Studies using phosphorylation site mutations are unclear. Presumably, if the phosphorylation sites were important for MAST3 activity, there should be major differences MAST3 activity between the WT and Ser/Ala mutants in the presence of activated PKA signaling. However, the data presented shows that the WT and mutated MAST3 proteins cause similar Ser46 ARPP-16 phosphorylation in each case. Please clarify.

We apologize for not explaining the results clearly, and we have reorganized and edited this section of the Results. Our initial studies indicated that PKA phosphorylates MAST3 at multiple sites, both in vitro and in intact cells. In vitro, PKA-dependent phosphorylation of MAST3 is associated with inhibition. We then investigated the roles of specific phosphorylation sites identified by mass spectrometry and bioinformatics (originally shown in Figures 5, 6 and Figure 3—figure supplement 5). To assess the effect of one of the sites, Thr389, we immunoprecipitated WT, T389D-MAST3 and T389A-MAST3 from Hek cells, and carried out in vitro assays to directly assess the effects of PKA-dependent phosphorylation on MAST3 activity. The T389D-MAST3-HA mutant was much less active than WT-MAST3-HA (Figure 5A). In contrast, even after pre-incubation with PKA and ATP, the T389A-MAST3-HA mutant was only slightly less active than WT MAST3-HA. The conclusion from this in vitro experiment was that T389 is phosphorylated by PKA, the T389D mutation acts as a phospho-mimetic to inhibit MAST3 activity, while the T389A mutant largely prevents the effect of PKA. This data was originally shown as a supplementary figure but we have moved it into the main figures (now Figure 5A).

We then analyzed the effects of mutations of four different phosphorylation sites (T389, S512, T628, and S747) on MAST3 activity in intact Hek cells. Sites were individually mutated to either alanine (non-phosphorylatable) or aspartate (phospho-mimetic). The T389D mutant, consistent with the in vitro assay, exhibited very low activity as demonstrated by the very low level of S46 phosphorylation (compare lanes 1 and 3 in the blot in Figure 5B, or the first and third black bar in Figure 5C). The effects of the S512D and T628D mutations were similar to each other, both displayed reduced MAST3 activity (see Figure 6), but this was not as much as the T389D mutation. The S747D mutant exhibited activity similar to the WT protein. With respect to the alanine mutations, the in vitro and intact cell experiments indicated that phosphorylation of Thr389 had the most robust effect on MAST3 activity. Mutation of Thr389 to alanine reduced the efficacy of forskolin to reduce Ser46 phosphorylation (Figure 5B and C). In contrast, the Ser512 or Thr628 to alanine mutants had no effect on the ability of forskolin to reduce Ser46 phosphorylation (Figure 6), presumably because of the predominant effect of Thr389 phosphorylation. We have clarified the description of these experiments in the revised manuscript.

8) The ability of Ser88 phosphorylated ARPP-16 to act as a dominant inhibitor of Ser64 phosphorylated ARPP-16 is intriguing. This suggests that the two ARPP-16 proteins might compete for binding to PP2A and that pSer88 ARP-16 has a higher affinity for PP2A than pSer64 ARPP-16. Alternatively, the binding may be non-competitive and pSer88 ARPP-16 may have dominant activity. Other mechanisms are also possible. Studies to evaluate mechanism are required, including the determination of binding affinity and binding competition.

We agree that this observation is intriguing, although as noted above and in the revised manuscript, based on the additional molecular modeling its functional significance may not be as great as the other two layers of regulation. We also agree that some sort of quantitative method that would determine binding affinity and mechanism is warranted. To achieve this we considered several options. (1) Kinetic analyses based on PP2A activity assays; (2) Direct binding experiments using ITC, SPR, or some sort of solution binding experiment combined with, for example, Scatchard analysis. While we have previously found that the A subunit of PP2A can bind directly to ARPP-16, it is likely that phospho-Ser46 binds to active site of the C subunit. Moreover, inhibition by phospho-ARPP-16 is not observed with the AC dimer (see (Andrade et al., 2017)), and specificity is exhibited for either the Bα or B56δ isoforms (Mochida et al., 2009; Mochida et al., 2010; Andrade et al., 2017). For these various reasons, we would need reasonable amounts of a purified heterotrimeric complex to carry out detailed analyses. This would go far beyond our current capabilities that are currently done with small-scale, partially purified, Hek cell preparations that exhibit experiment-to-experiment variation. We have begun to establish the resources to purify the required proteins, but we were not able to do this in the limited time available.

https://doi.org/10.7554/eLife.24998.035

Article and author information

Author details

  1. Veronica Musante

    Department of Psychiatry, Yale University School of Medicine, New Haven, United States
    Contribution
    VM, Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Writing—original draft, Project administration, Writing—review and editing
    Competing interests
    The authors declare that no competing interests exist.
    "This ORCID iD identifies the author of this article:" 0000-0001-5244-9158
  2. Lu Li

    The Babraham Institute, Cambridge, United Kingdom
    Contribution
    LL, Computational modelling, Data curation, Formal analysis, Visualization, Methodology, Writing—original draft
    Competing interests
    The authors declare that no competing interests exist.
    "This ORCID iD identifies the author of this article:" 0000-0002-6199-522X
  3. Jean Kanyo

    W.M. Keck Biotechnology Resource Laboratory, Yale University School Medicine, New Haven, United states
    Contribution
    JK, Formal analysis, Methodology
    Competing interests
    The authors declare that no competing interests exist.
  4. Tukiet T Lam

    W.M. Keck Biotechnology Resource Laboratory, Yale University School Medicine, New Haven, United states
    Contribution
    TTL, Formal analysis, Methodology, Writing—original draft
    Competing interests
    The authors declare that no competing interests exist.
  5. Christopher M Colangelo

    W.M. Keck Biotechnology Resource Laboratory, Yale University School Medicine, New Haven, United states
    Contribution
    CMC, Formal analysis, Methodology
    Competing interests
    The authors declare that no competing interests exist.
  6. Shuk Kei Cheng

    Laboratory of Molecular and Cellular Neuroscience, The Rockefeller University, New York, United States
    Contribution
    SKC, Methodology
    Competing interests
    The authors declare that no competing interests exist.
  7. A Harrison Brody

    Laboratory of Molecular and Cellular Neuroscience, The Rockefeller University, New York, United States
    Contribution
    AHB, Methodology
    Competing interests
    The authors declare that no competing interests exist.
  8. Paul Greengard

    Laboratory of Molecular and Cellular Neuroscience, The Rockefeller University, New York, United States
    Contribution
    PG, Resources
    Competing interests
    The authors declare that no competing interests exist.
  9. Nicolas Le Novère

    The Babraham Institute, Cambridge, United Kingdom
    Contribution
    NLN, Computational modelling, Conceptualization, Data curation, Writing—original draft
    Competing interests
    The authors declare that no competing interests exist.
    "This ORCID iD identifies the author of this article:" 0000-0002-6309-7327
  10. Angus C Nairn

    Department of Psychiatry, Yale University School of Medicine, New Haven, United States
    Contribution
    ACN, Conceptualization, Supervision, Funding acquisition, Writing—original draft, Project administration, Writing—review and editing
    For correspondence
    angus.nairn@yale.edu
    Competing interests
    The authors declare that no competing interests exist.
    "This ORCID iD identifies the author of this article:" 0000-0002-7075-0195

Funding

National Institute on Drug Abuse (DA018343)

  • Angus C Nairn

National Institutes of Health (DA10044 NS091336)

  • Veronica Musante
  • Paul Greengard
  • Angus C Nairn

European Commission (305299)

  • Lu Li

State of Connecticut Department of Mental Health and Addiction Services

  • Angus C Nairn

Biotechnology and Biological Sciences Research Council (BBS/E/B/000C0419)

  • Nicolas Le Novère

Brain and Behavior Research Foundation

  • Veronica Musante

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We would like to thank Mary LoPresti, Edward Voss, and Kathrin Wilczak for their assistance in MS sample preparation, and Piero Dalle Pezze for help with identifiability analysis and parameter estimation. Funding: This work was supported by NIH (DA10044 to ACN and PG). Proteomic analysis was supported by the Yale/NIDA Neuroproteomics Center (DA018343). VM was supported by a NARSAD Young Investigator award and NS091336. Support was also obtained from the State of Connecticut, Department of Mental Health and Addiction Services. LL was supported by the European Commission (grant 305299 AgedBrainSYSBIO). NL was supported by the Biotechnology and Biological Sciences Research Council (grant BBS/E/B/000C0419)

Reviewing Editor

  1. Roger J Davis, University of Massachusetts Medical School, United States

Publication history

  1. Received: January 10, 2017
  2. Accepted: June 13, 2017
  3. Accepted Manuscript published: June 14, 2017 (version 1)
  4. Version of Record published: July 18, 2017 (version 2)

Copyright

© 2017, Musante et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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