Abstract
Protein Kinase A (PKA) is an important cellular signaling hub whose activity has long been assumed to monotonically depend on the level of cyclic adenosine monophosphate (cAMP).
Using an optogenetic tool that can introduce precise amounts of cAMP in MDCKI cells, we demonstrate that PKA activity is instead characterized by a biphasic response, in which PKA activity increases and then decreases as a function of cAMP. We reveal that this behavior results from an elaborate integration by PKA of many cellular signals triggered by cAMP. In addition to the direct activation of PKA, cAMP also modulates the activity of p38 and ERK, which then converge on PKA to inhibit it. These interactions and their ensuing biphasic PKA profile have important physiological repercussions, triggering two distinct transcriptional programs elicited by low and high cAMP doses. These transcriptional responses in turn influence the ability of MDCKI cells to proliferate and form acini. Our data, supported by computational analyses, synthesize a set of network interconnections involving PKA and other important signaling pathways into a model that demonstrates how cells can capitalize on signal integration to create a diverse set of responses to cAMP concentration and produce complex input-output relationships.
Introduction
Cyclic adenosine monophosphate (cAMP) is a secondary messenger that is an essential cellular currency in all kingdoms of life (1–3). In mammalian systems, cAMP is the major regulator of Protein Kinase A (PKA), an important cellular kinase that exerts regulation on a wide spectrum of cellular components, ranging from metabolic enzymes and cytoskeleton components to various transcription factors, such as the cAMP response element binding protein (CREB)(4–7). PKA is a holoenzyme composed of two regulatory subunits (PKAr) and two catalytic subunits (PKAc). Upon binding of cAMP to the PKAr, all subunits dissociate, allowing PKAc to phosphorylate and regulate its targets(8). Through this influence on PKA, cAMP controls a large swath of crucial cellular processes, such as proliferation, death and differentiation(3).
Because of its physiological importance, the level of cAMP is subject to intricate regulation on its production by adenylyl cyclases (AC) and degradation by phosphodiesterases (PDE)(3). It has been largely assumed that the activity of PKA correlates monotonically with the cAMP concentration. However, this simple view of the functional relationship between cAMP and PKAhas recently been challenged. For example, it was demonstrated that the formation of memory in rodents shows a biphasic pattern that depends on cAMP level(9). When the hippocampus of mice was microinjected with low doses of cAMP analogs, they recalled their learnt behavior better than control mice. However, when treated with high doses of cAMP analogs, mice were worse at retaining the learnt behavior. A similar physiological biphasic relationship was also noted between cAMP and the secretion rate of malpighi tubes of Drosophila(10). When these organs were stimulated with low levels of cAMP, produced either with cAMP analogs or with an optogenetically-controlled AC, they showed an increase in secretion rate. By contrast, when the dose of cAMP was large, tubes had a secretion rate that was lower than unstimulated organs. Furthermore, recent data have documented that serotonin (5-HT), a hormone known to increase cAMP intracellular concentration (11), promotes the biphasic activation and inactivation of PKA in mammary epithelial cells, and that these PKA patterns result in an increase and decrease of transepithelial electrical resistance (TEER), respectively(12). Intriguingly, the same work also demostrated that the stress related p38 MAPK (p38) is important for an overall inhibition of TEER, therefore suggesting that p38 and perhaps other MAPKs are involved in the regulation of PKA in a cAMP dependent fashion(12, 13). These data give hints of a complex relationship between PKA and cAMP in which multiple cellular pathways modulate the dependence of PKA on cAMP to generate a biphasic relationship that induce distinct phenotypes at different cAMP concentrations.
To systematically study this relationship, we employed the bacterial light-activated adenylyl cyclase bPAC (14). bPAC was isolated from the Beggiatoa bacterial genus, and is functional when expressed in other species such as yeast or mice(15, 16), showing low activity in the dark and rapid activation upon exposure to blue light to specifically elevate intracellular cAMP(14).
Unlike environmental and chemical inputs, bPAC specifically controls cAMP levels without interference or crosstalk with other cellular variables. It therefore provided us with a convenient tool to unambiguously probe the effects of cAMP dose on PKA activity and determine dose-dependent transcriptional response and physiological outcomes. We expressed bPAC in Madin-Darby Canine Kidney Type I (MDCKI) cells (17) where the cAMP-PKA pathway has been shown to regulate acini formation(18). Many renal epithelial phenotypes, such as control of cyst size (19), podocyte differentiation(20), and fibrogenic programs in response to high glucose(21) are also controlled by the cAMP-PKA pathway. Therefore, understanding the effects of increasing cAMP concentrations in renal epithelial models, such as MDCK cells, is essential for understanding the biology of normal tissues as well as the pathological tissues in diseases such as diabetic glomerulopathy, nephrotic syndrome or polycystic kidney disease.
Our investigations using bPAC reveal that an increasing cAMP dose causally produces biphasic activity of PKA -- PKA activity increases and then decreases as a function of cAMP. ERK activity exhibits the same biphasic behavior as a function of cAMP dose but in the opposite direction, decreasing and then increasing as a function of cAMP. By contrast, p38 shows a monotonic dependence on cAMP. We show that the PKA inhibition at high cAMP dose is not generated by the known feedback loops regulating directly the level of cAMP, for example by regulation through increased PDE activity. Instead, we uncover that PKA activity is the result of a network of interactions involving cross-regulation with ERK and p38. We further demonstrate that the different behaviors of PKA at low versus high cAMP dose have important phenotypic consequences by eliciting two different cellular programs. We identify transcription factors that bind genes upregulated in both programs, as well as program-specific transcription factors, such as CREB that binds genes upregulated in low cAMP. Biological processes enriched in these two programs suggest that different cAMP doses differentially regulate many important phenotypes, such as proliferation and kidney morphogenesis. In particular, we show that concomitantly with changes in gene expression, the program elicited by a high dose of cAMP triggers increased proliferation and hampers the ability of the cell to form acini structures.
Together, these findings call for updating our understanding of PKA activity (at least in MDCK cells, and potentially in other systems) as a faithful monotonically dependent reporter of cAMP dose. Instead, we replace this notion with one in which PKA activity is finely tuned and modulated by other cellular pathways. This modulation has large phenotypic consequences, eliciting phenotypes reminiscent of involvement of these cells in health and disease of the kidney.
Results
PKA and ERK show a biphasic response to bPAC-generated cAMP inputs in MDCK cells
To examine the effects of increasing cAMP inputs in MDCK cells, we expressed bPAC (22), a blue light responsive cyclase that we targeted to the cytoplasm (MDCKI + bPAC) (Figure 1A). To test the ability of bPAC to rapidly produce cAMP in this cell line, we measured cAMP levels by ELISA in the presence and absence of blue light. In the absence of light both MDCKI and MDCKI+bPAC had similarly low levels of cAMP (∼1E-1 nM/ng protein, Figure 1B). After 20 minutes of continuous blue light exposure at maximum amplitude, only MDCKI+bPAC showed a large increase in intracellular cAMP (1318nM/ng protein, Figure 1B). To explore the range of intracellular cAMP concentrations that can be generated using bPAC, we further exposed MDCKI+bPAC cells to pulses of blue light of 30 second duration and varying duty cycles for 20 minutes. As expected, cAMP concentration increased with duty cycle (Figure 1C), reaching its maximum at continuous blue light exposure, demonstrating that bPAC can be used to generate a large range of precise cAMP levels spanning more than 3 orders of magnitude.
cAMP is a universal cellular messenger that acts upstream of PKA in all kingdoms of life (1). To determine the repercussions of increasing cAMP levels on PKA activity, we exposed MDCKI+bPAC cells to low (duty cycle = 1.7%) and high (duty cycle = 33%) bPAC inputs lasting 90 minutes and quantified PKA activity at different time points during light treatment by immunoblotting with antibodies that recognize the phosphorylated PKA target motif or phosphorylated cAMP Regulatory Element Binding Protein (CREB) (Supplementary Figure 1). Parental MDCKI cells did not show any changes in PKA activity for this regimen of light exposure (Supplementary Figure 1). By contrast, PKA in MDCKI + bPAC cells was responsive to light activation. Specifically, PKA activity increased in cells exposed to low cAMP, reaching its maximum at 10 minutes (Supplementary Figure 1) and then declining to a new steady-state after that. Remarkably, however, PKA activity decreased when cells were exposed to high cAMP concentrations, with minimum activity reached at 5 minutes and maintained over the time course (Supplementary Figure 1). To quantify cAMP regimes that lead to an increase versus a decrease in PKA activity, we subjected MDCKI and MDCKI + bPAC cells to light inputs of different duty cycles, ranging from 0 to continuous exposure and quantified their PKA activity at 20 minutes, a time that was chosen because of the timescales of PKA activation and inactivation observed above. We identified that PKA activity increases with cAMP concentration and reaches its peak activity at ∼1nM/mg protein (1.7% duty cycle). For cAMP concentrations above this value, PKA activity decreases, reaching values that are lower than its basal value for cAMP concentrations above 300nM/ng protein (Figure 1D and E). PKA activity, therefore, exhibits a markedly biphasic response to cAMP.
PKA activity is known to correlate negatively with mitogen activated kinase / extracellular signal-regulated kinase (ERK) pathway activity(23, 24). To further investigate the biphasic response elicited by cAMP, we measured ERK1/2 phosphorylation levels in MDCKI and MDCKI + bPAC cells subjected to the same light inputs as above. Remarkably, the ERK pathway showed a biphasic response as a function of cAMP, but one that was the opposite of PKA. In this response, doses of cAMP below 10nM/ng protein inhibited ERK1/2 below basal levels, with minimum activity appearing again around 1nM/ng cAMP protein (1.7% light duty cycle, Figure 1D and E, Supplementary Figure 1A). Above 10nM/ng cAMP inputs, ERK activation rose above its basal levels in the absence of stimulus. PKA has been implicated as a direct negative regulator of ERK activity and we hypothesized that ERK inhibition at low cAMP levels is the result of PKA activity(24). In agreement, when we treated cells subjected to no, low or high light inputs with H89 (a PKA inhibitor(25)), ERK activity increased above that of control cells (Supplementary Figure 2). Furthermore, the ERK activity of PKA-inhibited cells increased with duty cycle, suggesting that the ERK is activated by cAMP. Therefore, the biphasic behavior of ERK seems to be at least partially due to the competitive effects of direct activation by cAMP and inhibition by PKA.
Inhibition of PKA by high cAMP dose is independent of PDE activity and of the dissociation of PKA holoenzyme
To better conceptualize the molecular modalities by which cAMP can exert its physiological functions, we set to explore mechanisms of PKA regulation by cAMP and, in particular, of PKA inhibition by a high dose of cAMP, leading to its biphasic activity as a function of cAMP levels.
Previous investigations have described inactivation of PKA by two mechanisms involving feedback regulation that act primarily on cAMP. In the first mechanism, PKA activates PDEs that degrade cAMP thereby reducing PKA activity(26). In the second mechanism, PKA promotes the acidification of endosomes carrying AC-activating G protein–coupled receptors (GPCRs)(27). Since we were generating cAMP signals using an exogenous adenylyl cyclase, the second mechanism was less relevant. We therefore set out to test if PDE activity is required for PKA inhibition with high cAMP doses. We grew MDCKI + bPAC cells in the presence of DMSO or Rolipram to inhibit PDE activity for 1h, exposed them to varying doses of light for 20min, and then collected extracts from these cells and probed their PKA activity using immunoblotting (Supplementary Figure 3). As expected, inhibition of PDEs with Rolipram changed the input-output relationship from cAMP to PKA. PKA activity increased in the absence of light, and with cAMP inputs generated by duty cycles 0.3% and 1.7%, in cells exposed to Rolipram, when compared to DMSO (Figure 2A). Notably, however, for duty cycles larger than 1.7%, Rolipram treatment lead to the sharp inhibition of PKA activity, suggesting that cells reached the threshold of cAMP concentration required for PKA inhibition with a smaller input of light due to the lack of cAMP degradation (Figure 2A). We therefore conclude that modulation of cAMP by PDE is not likely to be a major contributor to the biphasic relationship between PKA activity and cAMP.
Next, we tested whether this biphasic response is generated by molecular interactions of the PKA complex itself. One of the hallmarks of PKA activation by cAMP is the dissociation of its catalytic subunits from the regulatory subunits that are bound to A-kinase anchoring proteins (AKAPs)(5). PKA inhibition is also usually associated with the reassociation of the holoenzyme. We therefore explored the state of the PKA holoenzyme under different regimes of cAMP input to determine whether the association of the PKA subunits is preserved at high cAMP concentrations. In order to visualize PKA localization in MDCKI cells, we expressed the catalytic subunit of human PKA (PRKACA) fused to mRuby2. We quantified nuclear enrichment of PRKACA-mRuby2 as a proxy for its dissociation from PKA regulatory units and AKAPs. We first quantified PRKACA-mRuby2 nuclear enrichment in cells exposed to Forskolin (adenylyl cyclase activator) or H89 (PKA inhibitor). As expected, PKA activation by Forskolin lead to dissociation of PRKACA and increased its nuclear localization in both MDCKI and MDCKI + bPAC cells (Figure 2B). Due to the low basal activity of PKA, H89 treatment did not lead to a significant change in PRKACA localization, which remained mostly in the cytoplasm. Next, we investigated how bPAC-generated cAMP inputs changed the localization of PRKACA. To do so, we exposed MDCKI and MDCKI + bPAC cells to 20min of light with duty cycle 1.7% (low cAMP) or 33% (high cAMP), which increased or decreased PKA activity, respectively (Figure 1E). Light-induced low cAMP doses increased the nuclear fraction of PRKACA in bPAC-expressing cells to levels similar to those achieved by Forskolin treatment (Figure 2B). By contrast, the localization of PRKACA in the parental cell line did not change in either light conditions (Figure 2B). In MDCKI + bPAC cells exposed to the PKA-inhibiting high dose of cAMP, PRKACA nuclear localization increased to levels above those of low cAMP doses and those achieved following Forskolin treatment (Figure 2B). These data indicate that high levels of cAMP are not likely to inhibit PKA by directly increasing the reassociation of the holoenzyme.
We therefore next explored whether the inhibition of PKA by high level of cAMP is a result of network interactions, potentially involving MAPKs, that stem from cAMP and then converge on PKA activity, subjecting it to competitive activating and inhibiting actions.
p38 and ERK activation are required for inhibition of PKA by high cAMP doses
Earlier work has shown that several pathways, including the p38 MAPK pathway, are activated by cAMP in both PKA-dependent and independent manner(28). Additionally, it has been shown that p38 can inhibit the PKA-dependent increase in TEER of epithelial cells(12) leading us to hypothesize that the p38 MAPK pathway might be directly activated by cAMP in MDCKI cells, and that it might converge onto PKA with inhibitory action (Figure 3A). To explore this possibility, we measured p38 phosphorylation in MDCKI and MDCKI + bPAC cells in response to increasing cAMP inputs, using immunoblotting. Light exposure did not increase p38 phosphorylation in parental MDCKI cells (Figure 3B, C). By contrast, bPAC-expressing cells exposed to increasing cAMP had a clear monotonic increase in phosphorylated p38 (Figure 3B, C).
To test whether p38 exerts inhibitory influence on PKA, we treated MDCKI + bPAC cells with the p38 inhibitor SB202190, exposed them to varying doses of light for 20min and measured PKA activity by immunoblotting. Inhibition of p38 increased PKA activity in MDCKI + bPAC cells across all light treatments compared to cells treated with vehicle (DMSO) (Figure 3D, Supplementary Figure 4), indicating that p38 indeed exerts an inhibitory influence on PKA. This inhibition, however, was not uniform across all doses of light (that is, at different cAMP concentrations). Instead, the difference in PKA activity between DMSO and SB202190-treated cells was larger at 0 duty cycle, suggesting that p38 was active and was important for setting the basal activity of PKA. This difference then decreased between duty cycles of 0 and 3.3% and increased again with increasing duty cycles larger than 3.3% (Figure 3E).
While p38 inhibition changed the relationship between PKA and cAMP, therefore clearly implicating p38 in regulation of PKA activity, it wasn’t able to completely abolish the decline of PKA activity at high cAMP levels. This implied that other inhibitory mechanisms are also at play. ERK signaling, whose activity negatively correlates with PKA (Figure 1E and F), is an obvious candidate for such regulation (Figure 3A). To investigate the role of ERK, we treated MDCKI + bPAC cells with U0216 (MEK inhibitor), then exposed cells to the same light doses as above and measured PKA activity by immunoblotting (Supplementary Figure 4). Cells with and without MEK inhibitor had the same relative PKA activity at light inputs below 3.3% duty cycle (Figure 3D), indicating that ERK activity had little influence on PKA at low levels of cAMP. However, U0126 treated cells had a reduced PKA inhibition at larger light inputs, suggesting that ERK activity was only effective in this regime. Taken together with the data demonstrating that ERK activity decreased for low cAMP and increased again for high cAMP (Figure 1F), we suspected that the most parsimonious model explaining the relationship between PKA and ERK is one in which they counteract each other in a double negative interaction (Figure 3A). Intuitively, in this model, ERK activity is a balance of activation by cAMP and inhibition by PKA. Low levels of cAMP cause large PKA activity, whose inhibitory effect dominates causing ERK activity to decrease (Supplementary Figure 2). At a certain threshold of cAMP though, the balance of power is flipped and ERK activity starts increasing, therefore subjecting PKA to inhibition at high cAMP doses (Figure 3E).
This reasoning does not take into account other interactions such as those of p38. We next explored whether the interactions between cAMP, PKA, ERK and p38, which were explored pairwise above, are able to produce the molecular phenotypes in the various experiments when considered together as an integrated system. To do so, we built a phenomenological mathematical model that captures these interactions in the cAMP-PKA signaling network. The input to this model is cAMP (different amounts produced by bPAC) and the output is PKA activity (as a surrogate for the phosphorylation of its targets). In this model, cAMP activates PKA, p38 and ERK with different kinetics, and ERK and p38 inhibit PKA activity therefore mediating an incoherent feedforward interaction from cAMP onto PKA (Figure 3A, see methods and Supplementary Information). To potentially account for the biphasic response of ERK and the pattern of its inhibition data, the model also assumes that PKA inhibits ERK activation. These interactions were modeled phenomenologically using enzymatic Hill functions in order to allow for the possibility that the interactions are not direct, but instead can have multiple intermediate steps. This functional form also allows for cooperativity and saturation in the substrate. In this form, the model was easily able to reproduce all the data from bPAC as well as kinase inhibition experiments. However, several structural features of the model, as well as choice of its parameters were essential for this.
With regard to the topology of the model, the inhibition of ERK by PKA and the dependence of both kinases on cAMP for their activation were vital for the biphasic behavior of ERK (Figure 3H) as a function of cAMP in DMSO-treated cells. While ERK inhibition of PKA was not required by the model to recapitulate the biphasic response of PKA to cAMP (as illustrated by the reduction in PKA activity at high doses of light in the presence of U0126,Figure 3F), this inhibition was essential to induce a reduction in PKA activity in the absence of p38 activity (Figure 3F). In other words, if ERK inhibition of PKA did not exist, PKA activity could never decrease when p38 is inhibited because no negative influences on PKA would exist. Second, the monotonic rise in p38 activity with increasing duty cycles (Figure 3I) indicated that no inhibition of p38 by PKA was needed, an insight further corroborated by the ability of the model to fully reproduce the data without any feedback from p38 onto PKA (Figure 3F). However, a basal activity of p38 in the absence of the cAMP input was required in order to reproduce the basal increase in PKA activity upon inhibition of p38 by SB202190 (Figure 3F and G).
In terms of parameter constraints, to fully represent the phenotype of p38 inhibition, the model required larger levels of cAMP in order to activate p38 and hence to trigger its negative effect on PKA, giving PKA activity the opportunity to increase at low cAMP levels (Figure 3F and J). This was achieved by a larger Km for the activation of p38 by cAMP (Kmp38) than that of PKA activation by cAMP (KmPKA) (∼27x larger in simulations, Supplementary Information, Supplemental Figure 5 A, B). When the Km’s of PKA and p38 were similar, the model achieved the activation of PKA by having a smaller Hill coefficient for the PKA activation (nPKA) than p38 (np38) (Supplementary Figure 5 A, B and C). A different scenario in which Kmp38 and KmPKA were similar but the Km of PKA inhibition by p38 (KmIp38) is large should be formally able to produce the same effect. However, such a parameter regime produces PKA activity that is the same in the absence of cAMP between SB202190-treated cells and DMSO treated cells, which is contradictory with our data (Figure3F and J, Supplemental Figure 5).
Additionally, to reproduce the effects of ERK inhibition, the model required a larger Km ERK (Km for the activation of ERK by cAMP) than KmPKA (∼32x larger, Supplementary Information, Supplemental Figure 5A, B) or a smaller nPKA than nERK (Supplementary figure 5 A, B and C), and a high Hill coefficient for the ERK inhibition of PKA (nERK = 5.37, Supplementary Information, Supplemental Figure 5A and E). These parameters were necessary to reproduce the small effect of ERK inhibition on PKA activity for small cAMP inputs (Figure 3F and G) and its strong effect at high cAMP inputs. Intuitively, at low cAMP concentrations, PKA inhibition of ERK dominates and hence ERK plays a small role in modulating PKA activity (Figure 3J and K). However, as cAMP concentration increases and ERK passes a predicted sharp threshold where its activity starts increasing again (representing an inflection point in the ERK activity, Figure 1E), it starts exerting a negative effect on PKA (Figures 3J and K). This is the regime where inhibition of ERK has a notable effect as indicated by the data.
Finally, the model predicted that the inhibitory effect of p38 on PKA should increase noticeably only after cAMP inputs larger than Kmp38 (duty cycle 6.7 and larger) and that it should reach saturation for duty cycle 100% (Figure 3L). The saturation of this inhibitory effect was essential for reproducing the quantitative pattern of PKA inhibition in the presence of U0126, where the rate of inhibition of PKA seemed to decrease at high duty cycles (from 33% to 100%, Figure 3F and G). Since in this experiment, only p38 is present as an inhibitory influence on PKA, and since its level continues to increase at these high duty cycles, the fact that PKA activity assumes similar values for different duty cycles while cAMP increases indicates that the influence of p38 on PKA has to reach a saturation. This saturation effect should also be present in cells treated with DMSO, which was absent in the experimental data (Figure 3F and G), suggesting that additional interactions between the MAPKs that become important at high cAMP levels might also be present beyond the parsimonious model we have used.
Taken together, our data and computational modeling suggest that p38 activation modulates the activity of PKA through a cAMP-driven incoherent feedforward loop that plays an important role at all cAMP concentrations and that ERK engages in a double-negative feedback interaction whose importance seems to manifest at higher cAMP concentration. These interactions are integrated to produce a biphasic program of PKA activity for different cAMP concentrations. Given that PKA is a master regulator of cell physiology, we next turned to investigating whether this biphasic program has biological repercussions.
bPAC-generated cAMP inputs elicit differential transcriptional responses
PKA and ERK are known to target and modulate the activity of a wide array of transcription factors(6, 7). We therefore postulated that the ability of bPAC-generated cAMP to induce their biphasic response might have profound and distinct effects on the cellular transcriptome. To test this hypothesis, we carried out mRNA sequencing of MDCKI and MDCKI+bPAC cells under no, low, or high cAMP inputs (0%, 1.7% and 33% blue light duty cycle) for 2 hours.
In order to identify the effect of bPAC expression on cellular function in the absence of light, we mined our dataset for genes that were differentially expressed in MDCKI + bPAC with no light versus MDCKI parental cells (duty cycle = 0%). Additionally, we measured the effects of blue light exposure on the MDCKI transcriptome by identifying genes that were differentially expressed in MDCKI parental cells exposed to low or high light inputs (1.7% or 33%) versus no light input (0%). We used DEseq2(29) to quantify Fold Change (FC) between conditions Using a threshold for fold change in expression larger than 1.5 and an adjusted p-value smaller than 0.05 (|FC|>1.5, p<0.05, see methods for justification of this choice), we identified 371 genes that were differentially expressed due to bPAC expression and in the absence of light (Supplementary Figure 6).These genes were enriched in Gene ontology(30) (GO) terms related to cellular adhesion and mitosis. Reassuringly, these genes were not correlated with cAMP induced expression changes (see below, Supplementary Figure 6). We also identified 434 genes that were differentially regulated due to light exposure even in parental cell lines. These genes were removed from subsequent analyses.
Our aim was to capture all genes that were differentially expressed in response to light-induced cAMP inputs, and then classify them according to their patterns of cAMP dependence (monotonic or biphasic dependence on cAMP). Using the same thresholds as above (|FC|>1.5, p<0.05), we identified 3471 genes that are differentially expressed in response to one or both cAMP inputs, as well as genes that are differentially expressed between cAMP doses (Figure 4A, Supplementary table 1). We used hierarchical clustering to group them according to their patterns. We identified 8 clusters that were further combined in 4 qualitatively different classes. Genes in clusters 1 and 2 were strongly repressed in low cAMP and weakly repressed or activated in high cAMP, hence showing correlation with ERK activity. On the other hand, genes in clusters 3 and 4 were strongly activated in low cAMP and weakly activated or repressed in high cAMP dose, therefore correlating with biphasic PKA activity. Finally, genes in clusters 5 and 6 were monotonically activated by cAMP while genes in clusters 7 and 8 were monotonically repressed by cAMP (Figure 4A and B). We confirmed the RNA sequencing results for four cAMP regulated genes with qPCR experiments on independent RNA samples (Supplementary Figure 7).
To distill the various transcriptional programs corresponding to the different classes, we performed Enrichr analysis(31) on genes from each class, focusing on transcription factors and kinases that regulate these genes and GO enrichment analysis to identify biological processes in which they are involved. Genes whose expression increased (clusters 5 and 6) or decreased (clusters 7 and 8) monotonically with cAMP concentration showed enrichment for the binding of specific transcription factors, such as NELFA or ELKF (Supplementary Figure 8), and regulation by kinases, such as SYK and AKT (Supplementary Figure 8). These genes were also enriched for GO terms related to chromatin remodeling, protein kinase A signaling, regulation of epithelium morphogenesis and DNA repair (Supplementary Figure 9). While the promotors of the genes in all clusters were enriched for the cAMP responsive element modulator (CREM) protein binding, suggesting a common dependency on cAMP, we decided to focus our thorough analyses on the clusters of genes that have a biphasic behavior, therefore reflecting the PKA or the ERK pattern, in order to identify the distinct genetic programs encoded by low and high cAMP doses (clusters 1-2 and 3-4). We expected that genes whose expression correlate with ERK activity (clusters 1 and 2) could be controlled by transcriptional regulators that are activated by ERK, or by transcriptional regulators that are inactivated in the absence of PKA activity. We found examples that matched the two hypotheses. First, this set of genes was specifically enriched for Vitamin D Receptor (VDR) and GA binding protein (GABP) binding (adjusted p-values 1.6×10−14 and 1.0×10−10, Supplementary Figure 8), which have been shown to have activity that is modulated by ERK(32, 33). We also found enrichment for genes that are up-regulated upon CREB1 depletion (adjusted p-value 2.2×10−13, Supplementary Figure 8), whose activity is PKA-dependent(34). Interestingly, we also found that the set of genes whose expression correlates with ERK activity (clusters 1 and 2) was enriched for genes that are upregulated in a protein kinase B (AKT1) knockout and downregulated in the presence of an AKT1 active mutant (adjusted p-values 2.1×10−5 and 1.8×10−9, Supplementary Figure 8). These data suggest that AKT1 might also be differentially regulated by cAMP dose and that its activity should correlate negatively with ERK activity. To confirm these predictions, we measured phosphorylation level of AKT in parental MDCKI and MDCKI + bPAC cells exposed to low or high cAMP inputs. While there were no changes in AKT activity when the parental cell line was exposed to light, bPAC-carrying cells increased phospho-AKT levels with low doses of cAMP and quenched them at high cAMP doses (Supplementary Figure 10), again showing a biphasic response.
Next, we extended our analysis to the gene set whose expression correlated with PKA activity (clusters 3 and 4), again hypothesizing that their pattern of expression either followed PKA activity (they are activated by PKA-responsive transcription factors), or followed the absence of ERK regulated activators (they are repressed by ERK-responsive transcriptional regulators). These clusters were enriched in genes that are known targets of CREB1 and the histone demethylase KDM5B (adjusted p-values 1.0×10−14 and 1.3×10−13, Supplementary Figure 8), an effect that is likely mediated by PKA-driven regulation of chromatin remodelers and its activation of CREB1 transcription factor. In addition, we found a clear enrichment for genes that are known to be upregulated following knockout of the transcription factor jun-B (JUNB, adjusted p-value 1.2×10 −13, Supplementary Figure 8), which is known to be activated by ERK as an immediate-early gene(35). As a result, ERK is likely to exert a repressive effect on these genes, and their pattern (e.g. activation at low cAMP) is therefore a result of differential alleviation of this repression as a function of cAMP.
Finally, we performed GO enrichment analysis for the 2 gene classes (Supplementary Figure 9). Genes belonging to clusters 1 and 2 (ERK correlated) were enriched in GO terms that encompassed desmosome organization, stress activated MAPK cascade and positive regulation of respiratory burst (Figure 4C, Supplementary Figure 9). By contrast, genes in clusters 3 and 4 (PKA correlated) had strong enrichment for terms related to chromatin remodeling, kidney morphogenesis and negative regulation of proliferation (Figure 4D, Supplementary Figure 9).
Together these findings suggest that the biphasic signaling responses of PKA and MAPK activity in MDCKI cells to cAMP dose propagates to gene expression, therefore potentially programming a large array of cellular phenotypes, ranging from proliferation, cell-cell and cell-extracellular matrix interactions, metabolism, and kidney morphogenesis.
Cellular proliferation is positively influenced by high cAMP inputs
Many genes in clusters 3 and 4 were associated with negative control of cellular proliferation, and these were repressed in the presence of high cAMP levels. This suggested that at a high cAMP dose, repression of these genes positions MDCKI cells to a state similar to cells transiting to a mesenchymal state(36) or in elongating regions of epithelial tubes, which have weaker cell-cell interactions and higher rates of proliferation(37, 38). To test this possibility, we exposed parental MDCKI and MDCKI + bPAC cells to no, low or high cAMP doses (duty cycle of 0, 1.7 and 33%) for 2 hours and measured their mitotic fraction by histone H3S10 phosphorylation after 14h. As expected, there were no significant changes in the mitotic fraction of MDCKI parental cells exposed to light. Yet, MDCKI + bPAC cells stimulated with a high cAMP dose had a marked ∼5-fold increase in their proliferation rate when compared to unstimulated cells (Figure 5A). Cells exposed to the low cAMP input did not have a statistically significant change in the mitotic rate (∼1.5-fold reduction in comparison to cells exposed to 0% duty cycle) (Figure 5A), indicating that only high cAMP treatment can significantly change the proliferation rate of MDCKI cells. These data support the notion that different doses of the same secondary messenger (cAMP), which result in different signaling and transcriptional patterns, can be decoded by cells to regulate proliferation in a differential way.
Formation of MDCKI acini is differentially controlled by cAMP dose
We noted that kidney developmental genes and cell adhesion genes belonged to the class of genes that correlate with PKA activity (clusters 3 and 4, Figure 4A and B). In addition, both PKA and ERK signaling pathways have been shown to regulate different stages of kidney development(19, 39, 39). Therefore, we predicted that different cAMP doses, through their biphasic influence on PKA and ERK, might influence the formation and maintenance of kidney structures differently. To examine this effect, we focused on acinar development of MDCKI cells. MDCK acinus formation is a well-studied model of kidney epithelial structure and function(40, 41). Correct formation and maintenance of MDCKI acini relies on the precise coordination of events that incorporate cell interactions with basement membrane, cell polarization, formation of tight junctions and dependence on an abundance of extracellular signals(18).
It is known that PKA activity and ERK inhibition is required for the rapid formation of acini(23). Therefore, we hypothesized that low cAMP doses and accompanying high PKA activity leads to the development of acini of normal size, while high doses of cAMP and associated ERK activation lead to increased cellular mobility and concomitant incorrect acinar development. To test this hypothesis, we quantified acinar size in MDCKI and MDCKI+bPAC cells grown in collagen gels exposed to no, low or high cAMP inputs for 10 days. MDCKI cells exposed to all light conditions showed no significant change in the area of their acini (Figure 5B), demonstrating that the effects of light are negligible on acini formation and maintenance. By contrast, cAMP dose had a striking effect on the area of bPAC-expressing acini (Figure 5B). Specifically, we identified that low and high cAMP inputs had a significant effect on the distribution of acini area, in which the low dose of cAMP increased the area of acini (median increase of 1.6-fold when compared to no cAMP), and acini exposed to high doses of cAMP showed a reduction in their area (median reduction of 1.7-fold when compared to no cAMP) (Figure 5B). These data illustrate that tight control of intracellular cAMP concentration and signaling activity is vital for the correct formation of multicellular structures, and that MDCKI cells can use cAMP concentration and its paradoxical effect on downstream pathways to generate an array of multicellular epithelial phenotypes.
Discussion
In this work, we used a combination of precise optogenetic stimulation to control cAMP levels in living cells, with various cell biological readouts of its impact, to investigate the relationship between cAMP dose and the PKA signaling pathway. We uncovered that PKA and ERK activity is biphasic as a function of cAMP, with PKA activity increasing and then decreasing as a function of cAMP and ERK activity showing the opposite behavior. To understand the repercussions of these different dependencies, we used mRNA sequencing under two cAMP regimes (low and high) to demonstrate that they indeed correspond to two different molecular programs and physiological outcomes. Molecularly, we explored the intricate roles of p38 and the ERK pathways in producing the biphasic PKA phenotype and presented a data-supported model in which a poorly studied interaction between PKA and MAPKs inhibits PKA signaling pathway in a cAMP-dependent way. As a result, PKA is under positive and negative influences from cAMP, with the negative influence mediated by MAPKs. The interaction of these two modes of regulation, and their relative strength, induces a non-monotonic dependence of PKA on cAMP in the MDCKI cells we study here.
The biphasic response of PKA to cAMP has been previously observed in malpighi tubes of flies(10), hypothalamus of mice(9), and human mammary cells(12) stimulated with small molecules. However, the non-monotonic behaviors reported in most of these earlier works resulted from chemical inputs that perturbed many cellular nodes, other than cAMP. Our use of bPAC allowed us to circumvent this pleiotropy and uncouple feedback regulation on cAMP production from PKA activity. As a result, we could unambiguously link PKA activity solely to the amount of cAMP produced by bPAC and hence causally link cAMP concentration to the biphasic response of the PKA pathway. Additionally, this precise cAMP input afforded by bPAC enabled us to causally establish additional downstream targets of cAMP, including ERK, AKT and p38.
The biphasic nature of PKA and ERK responses to cAMP is implemented both by the incoherent feedforward loop (IFFL) motif from cAMP to PKA through the activities of p38 and ERK, and by the mutual inhibition of PKA and ERK. It is unknown how p38 and ERK produce their negative effect on PKA signaling. Our transcriptional dataset indicated that DUSPs are upregulated upon high cAMP doses, suggesting that they might mediate some of this interaction. However, further studies will be necessary to discover if their phosphatase activity is altered, if PKA and its targets are also targeted by these DUSPs, or if additional mechanisms are involved. Furthermore, it is unclear how ERK and p38 are activated by cAMP. While previous studies have shown activation of MAPKs by cAMP through the activity of PKA(42), our results suggest that, in MDCKI cells, additional cAMP-dependent mechanisms are involved. Previous results suggest that EPAC, PKA regulatory subunits and β-arrestins can initiate the activation of MAPK signaling pathways(28, 43–45). It is therefore possible that one or more of these mechanisms are responsible for the activation of MAPKs at high cAMP doses.
Irrespective of the precise molecular mechanisms underlying cAMP effect on MAPKs and the effect of MAPKs on PKA, the presence of at least two IFFL structures in the system is at the heart of its biphasic behavior. An IFFL is a circuit motif that that is usually assumed to respond to a step input with a transient response that then adapts close to its unstimulated state (46). However, a less studied operational regime of IFFL is one in which they can give rise to dose-dependent biphasic responses(47, 48), such as the ones shown here. We explored these regimes in the context of the cAMP-PKA computational model and identified its specific parameter requirements. It is interesting to think that in other cell types, or even in disease states, changes in parameter values or network topology can give origin to radically different signaling responses out of the same molecular players. For example, if PKA is unable to inhibit ERK, ERK activity would increase monotonically with cAMP concentration. As a result, the signaling state of cells would be that of active PKA and ERK, instead of active PKA and inhibited ERK, at low cAMP concentration. It is far from clear what new functionalities or pathologies might ensue from these different signaling states. This is particularly important in light of the fact that the capacity of cAMP to coordinate the activity of important signaling pathways in MDCKI cells led to important changes in the expression of many genes. Remarkably, several of those genes that were cAMP responsive followed either a PKA or an ERK pattern, and correspondingly implemented two different programs. In the high cAMP program (repressed PKA and activated ERK) the proliferation rate of cells was increased and their ability to form acini reduced. By contrast, at a lower cAMP dose, proliferation rate was smaller than at a high cAMP dose and the size of acini was larger, in accordance with the previously described effects of PKA on acinar morphogenesis(23). Interestingly, in Polycystic Kidney disease (PKD) cells, PKA is thought to activate MAPK and that activity of both pathways is required for the measured increase in proliferation rate and acinus size(19). It is unclear if cells are producing abnormal cAMP doses or if the decoding of cAMP dose is deficient. While therapies for PKD have focused on reducing cAMP levels(49), if the network topology present in MDCKI cells is conserved in PKD cells, an alternative approach would be to increase cAMP, or directly activate p38, to inhibit PKA activity and reduce cystogenesis. Finally, while the particular interactions in the cAMP-PKA-MAPK network we uncover here might be specific to epithelial cells, it is fascinating to think about how these links might be modulated, to shift the quantitative relationship between cAMP and PKA in different cell types. For example, a quantitative change in the inhibition of PKA by p38 or ERK might shift where PKA activity reaches its peak as a function of cAMP. A different change, balancing the timescale of the inhibiting and activating links on PKA, might abolish the biphasic relationship altogether. One can also imagine that some cell types might modulate these links dynamically, using inputs other than cAMP to impinge on the relationship of p38 and ERK on PKA, weakening or strengthening it based on the particular circumstances encoded in these inputs.
Methods and Materials
Cell culture
MDCKI cell line was a kind gift from Pavel Nedvestky. No mycoplasma contamination was found. MDCKI + bPAC cells were generated by infecting MDCKI cells with pLenti-PGK-bPAC::NES (Addgene #130267). PKAc expressing cells were obtained by transfecting MDCKI and MDCKI + bPAC cells with pPB-CAG-PRKACA::mRuby2 (Addgene #130268). Cells were maintained in MEM (Gibco #11095072) supplemented with 10% Fetal Calf Serum (VWR #89510-184) and 1X Anti-Anti (Gibco #15240062), and kept at 37 °C in a humidified incubator with 5% CO2.
Antibodies and reagents
To probe signaling activity we used the following antibodies: anti-Phospho-PKA Substrate (RRXS*/T*) (100G7E) Rabbit mAb (Cell signaling technology #9624S, 1:2000), anti-Phospho-CREB (Ser133) (87G3) Rabbit mAb (Cell signaling technology # 9198S, 1:2000), anti-p44/42 MAPK (Erk1/2) (L34F12) Mouse mAb (Cell signaling technology ##4696, 1:2000), anti-Phospho-p38 MAPK (Thr180/Tyr182) (D3F9) XP® Rabbit mAb (Cell signaling technology #4511, 1:2000), anti-αTubulin Antibody (DM1A) (Santa Cruz biotechnology #sc-32293, 1:2000), anti-Phospho-Akt (Thr308) (D25E6) XP Rabbit mAb (Cell signaling technology #13038S, 1:2000), anti-Phospho-Histone H3 (Ser10) Antibody (Cell signaling technology #9701, 1:500), IRDye 800CW Donkey anti-Rabbit (LI-COR #925-32213, 1:10000), IRDye 680RD Donkey anti-Mouse (LI-COR #925-68072, 1:10000) and Anti-rabbit IgG (H+L), F(ab’)2 Fragment (Alexa Fluor® 488 Conjugate) (Cell Signaling Technology #4412, 1:1000).
The following small molecules were used: SB202190 (Selleck Chemicals, #S1077, 10μM), U0126 (MedChem Express #HY-12031, 10μM), rolipram ((R)-(-)-Rolipram, Tocris #1349, 100μM), H89 (H 89 dihydrochloride, Tocris #2910, 10μM) and Forskolin (Tocris # 1099/10, 50μM).
Quantification of cAMP
1×10^6 cells were seeded in wells of 6-well plates (FALCON # 353046) in MEM without phenol red (Gibco # 51200038) supplemented with 0.5% Fetal Calf Serum (VWR #89510-184). After 16h, cells were exposed to no light or blue light (2.2 mW.cm−2) with different duty cycles for 20min. After light exposure cells were transferred to ice and cAMP concentration was determined using the cAMP ELISA Kit (Cell biolabs #STA-501), following the manufacturer’s instructions. To determine protein concentration, ELISA samples were assayed using the Pierce BCA Protein Assay Kit (Thermo Scientific Pierce #23227), following the manufacturer’s instructions. Measurements for cAMP ELISA and BCA Protein Assay kits were performed in a FlexStation 3 Multi-Mode Microplate Reader (Molecular Devices). The cAMP dose response to light was fit to a hyperbolic equation and confidence intervals were extracted by bootstrapping in a custom Python Script available on GitHub (https://github.com/jpfon/cAMP).
Quantitative western blot
1×10^6 cells were seeded in wells of 6-well plates (FALCON # 353046) in MEM without phenol red (Gibco # 51200038) supplemented with 0.5% Fetal Calf Serum (VWR #89510-184). After 16h, cells were treated with inhibitors or vehicle (DMSO, SIGMA life science #D2650) for 1h. Cells were then exposed to no light or blue light (2.2 mW.cm−2) with different duty cycles for 20min. Cells were lysed in cold M-PER (Thermo Scientific Pierce #PI78501) supplemented with protease (Thermo Scientific Pierce #PIA32953) and phosphatase (Thermo Scientific Pierce #PI-88667) inhibitors for 20min. Proteins were separated in 7.5% Mini-PROTEAN® TGX™ Precast Protein Gels (Biorad), probed with primary antibodies diluted in blocking buffer (Rockland Immunochemical #MB-070) for 16h and with secondary antibodies diluted in blocking buffer for 2h. Proteins were detected by fluorescence in a Odyssey CLx (LI-COR). Phospho-PKA Substrate quantification was performed for proteins above 75KDa. Tubulin was used as a loading control. Quantification was done using ImageJ (NIH) and a custom Python Script available on GitHub (https://github.com/jpfon/cAMP).
RNA library preparation and sequencing
1×10^6 cells were seeded in wells of 6-well plates (FALCON # 353046) in MEM without phenol red (Gibco # 51200038) supplemented with 0.5% Fetal Calf Serum (VWR #89510-184). After 16h, cells were exposed to no light or blue light (2.2 mW.cm−2) with different duty cycles for 2h. After light exposure cells were transferred to ice and total RNA was isolated using SPLIT RNA Extraction Kit (Lexogen # 008.48), following the manufacturer’s instructions. RNA quality was assessed using RNA 6000 Nano chips (Agilent # 5067-1512) in a Bioanalyzer 2100 (Agilent). RNA libraries for Illumina sequencing were prepared from 2ug of total RNA, using the QuantSeq 3’ mRNA-Seq Library Prep Kit FWD for Illumina (Lexogen # 015.96). Library quality was assessed using High Sensitivity DNA kit (Agilent # 5067-4626) in a Bioanalyzer 2100 (Agilent). Sequencing was performed at the Center for Advanced Technology (UCSF), in a HiSeq 4000 (Illumina). One lane was used to generate 50bp single reads. Sequencing data is available GEO (GSE134650)
RNA sequencing analysis
Low quality reads and reads containing adapters were removed from raw data. Gene counts were generated using the standard workflow of Bluebee NGS Genomics Analysis Software (QuantSeq-FWD) for the dog genome (https://platform.bluebee.com). Differentially expressed (DE) genes were identified using the DESeq2 R package(29), with |logFC| >1.5 adjp >0.05, in order to reduce the false discovery rate and genes whose expression change was small. Clustering of DE genes was performed using a custom R script available on GitHub (https://github.com/jpfon/cAMP). Gene Ontology term enrichment for genes in each cluster was performed using DAVID(30) and displayed in a 2D scatterplot based on the semantic similarity (Revigo(50)). Transcription factor and kinase regulation enrichment analysis was performed using Enrichr (31).
Quantitative RT-PCR
Total RNA was prepared as described in the RNA library preparation section. First strand synthesis was performed on 5ug of RNA using Superscript II (Invitrogen #18064-014) and following the manufacturer’s instructions. qPCR was performed in a CFX connect (Bio Rad), using the PerfeCTa SYBR® Green FastMix (Quantabio #95072) and following the manufacturer’s instructions for Fast 2-Step Cycling. GUSB expression was used as normalization factor. Primers used are shown in Supplementary Table 2.
Quantification of mitotic fraction
4×10^4 cells were seeded in wells of 8-well slides (Ibidi # 80826) in MEM without phenol red (Gibco # 51200038) supplemented with 2% Fetal Calf Serum (VWR #89510-184). After 16h, cells were exposed to no light or blue light (2.2 mW/cm^2) with different duty cycles for 2h. After 14h, cells were fixed in 4% formaldehyde (Thermo Scientific Pierce #PI-28906) for 20min on ice, permeabilized with PBS-T (PBS (UCSF Cell Culture Facility) + 0.3% Tween-20 (Sigma-Aldrich #P1379)), blocked with normal goat serum (Abcam, #ab7481) for 1h, incubated with anti-H3S10p for 16h at 4oC and with secondary antibody for 3h at room temperature, and stained with Hoechst 33342 (Molecular Probes #H3570, 5μg/ml) for 10min. 3 PBS-T washes were performed between each step of the protocol, with a final PBS wash. Cells were imaged on a Nikon Ti inverted scope with arc-lamp illumination. Hoechst 33342 was detected using a 350/50nm excitation filter and a 460/50nm emission filter and H3S10p detected using a 470/40nm excitation filter and 525/50nm emission filter (Semrock, Rochester, NY). Cell nuclei were segmented and mitotic fraction was quantified using a custom python script available on github (https://github.com/jpfon/cAMP)
Quantification of acinar area
MDCKI acini were prepared as described in (Elia and Lippincott-Schwart 2010). Over 11 days, acini were exposed to no, low (Duty Cycle = 1.7%) or high (Duty Cycle = 33%) blue light (2mW/cm^2) doses, for 1h of every 48h. Media was replenished at the same rate. Acini were imaged on a Nikon Ti inverted scope using brightfield. Acinar area was quantified using ImageJ (NIH).
Imaging of PKAc and quantification of nuclear enrichment
1×10^5 cells were seeded in wells of 8-well slides (Ibidi # 80826) in MEM without phenol red (Gibco # 51200038) supplemented with 0.5% Fetal Calf Serum (VWR #89510-184). After 16h, cells were treated with small molecules or vehicle (DMSO, SIGMA life science #D2650) for 1h. Cells were then exposed to no light or blue light (2mW/cm^2) with different duty cycles for 20min to. Cells were immediately fixed in 4% formaldehyde (Thermo Scientific Pierce #PI-28906) and stained with Hoechst 33342 (5μg/ml). Cells were imaged on a Zeiss microscope equipped with a Yokagawa CSUX1-A1N-E confocal spinning disk. Images were collected with a 40 x 1.1 NA water immersion objective and Photometrics Evolve 512 EMCCD camera. Hoechst 33342 and PRKACA::mRuby2 were detected using the following excitation lines and emission filters: excitation at 405nm, collection between 425 and 475nm for Hoechst 3342; excitation at 561nm, collection between 590 and 650nm for PRKACA::mRuby2. Segmentation of nuclei and quantification of PRKACA Nuclear to Cytoplasmic ratio were performed using a custom python script available on Github (https://github.com/jpfon/cAMP).
Data availability
Raw data in this study is available at the UCSF repository (https://doi.org/10.7272/Q6ST7N00).
Statistical analysis
Differences in mitotic fraction were calculated by Fischer’s exact test, in acini area by the Kolmogorov-Smirnov test, in PRKACA nuclear enrichment by one-way ANOVA with Tukey-HSD post-tests, and in the effects of Rolipram on PKA activity with a two-way ANOVA. All tests were implemented with Python scripts available on Github (https://github.com/jpfon/cAMP)
Computational model
The computational model representing the cAMP-PKA signaling network is described in the Supplementary Information. Model simulations and optimization were performed with Python scripts available on Github (https://github.com/jpfon/cAMP)
Supplementary Figure 1.
Time course of CREB, PKA and ERK1/2 activity of MDCKI and MDCKI + bPAC cells treated with two cAMP doses
(A) Immunoblots of PKA, CREB and ERK activity (measured with Phospho-PKA substrate, Phospho-CREB and Phospho-ERK antibodies) of MDCKI and MDCKI + bPAC cells treated for 0, 5, 10, 20, 30, 60 and 90 minutes with low and high cAMP doses (Duty Cycle = 1.7% and 33%, respectively). (B) Quantification of immunoblots show in (A). Quantification was performed in at least 3 replicates.
Supplementary Figure 2
ERK activity of MDCKI + bPAC cells treated with light and PKA inhibitor H89
(A) Immunoblots of ERK activity (measured with Phospho-ERK antibody) of MDCKI+ cells treated with DMSO or PKA inhibitor (H89 (10μM)) and subsequently exposed to 20min of blue light with different duty cycles. (B) Quantification of immunoblot shown in (A)
Supplementary Figure 3
PKA activity of MDCKI + bPAC cells treated with light and PDE inhibitor Rolipram
Immunoblots of PKA activity (measured with Phospho-PKA substrate antibody) of MDCKI + bPAC cells treated with DMSO or PDE inhibitors (Rolipram (10μM)) and subsequently exposed to 20min of blue light with different duty cycles.
Supplementary Figure 4
PKA activity of MDCKI + bPAC cells treated with light and different MAPKs inhibitors
Immunoblots of PKA activity (measured with Phospho-PKA substrate antibody) of MDCKI + bPAC cells treated with DMSO, MEK inhibitor (U0126 (10 μM)) or p38 inhibitor (SB203580 (10 μM)) and subsequently exposed to 20min of blue light with different duty cycles.
Supplementary Figure 5
Distributions of cAMP-PKA model parameters
(A) Distributions (diagonal) and pair-wise joint distributions of parameter values in the cAMP-PKA computational model that were able to describe PKA, ERK and p38 activity in the presence of DMSO, and PKA in the presence of U0126 and SB202190. (B) Distributions of KmPKA (blue), KmERK (green) and Kmp38 (red). Most fitted KmPKA parameters are smaller than KmERK and Kmp38. (C) Relationship between KmPKA and nPKA. For large KmPKA, nPKA is always small. (D) Relationship between KmPKA, and the ratio of nPKA to nERK (blue) or to np38 (red). nPKA is much smaller than nERK and np38 for large KmPKA. (E) Distributions of nIPKA (blue), nIERK (green) and nIp38 (red). Fitted nIERK parameters are always large. Detailed description of model and simulations in Supplementary Information.
Supplementary Figure 6
Effect of bPAC expression in MDCKI transcriptome
(A) Volcano plot identifying genes that are repressed (magenta) or overexpressed (green) upon expression of bPAC in MDCKI cells. (B) Normalized expression level of genes regulated by bPAC presence (n = 371) in MDCKI + bPAC cells after 2h of low and high cAMP doses (Duty Cycles of 1.7% and 33%, respectively). Repression or upregulation upon bPAC expression does not inform of behavior upon additional cAMP input. Membership in clusters identified in Figure 4 is shown on the left (92 genes) as well as positive or negative effect of bPAC expression.
Supplementary Figure 7.
Validation of RNAseq results with qPCR
(A) RNA sequencing counts and (B) qPCR quantification for NR4A2, PAX8, PER1 and GATA3 genes in MDCKI and MDCKI + bPAC cells exposed to blue light for 2h with different duty cycles for period (T) equal to 30s (0%, 1.7% and 33 or 100%). qPCR was performed from at least 3 independent RNA isolations.
Supplementary Figure 8.
Transcription factor and Kinase Perturbation associated with differentially expressed genes
(A) Top twenty transcription factors from ChEA 2016 database in Enrichr tool whose binding is enriched in the promotors of the genes included in the clusters identified in Figure 4. (B) Top twenty transcription factor perturbations from Transcription Factor Perturbation database in Enrichr tool whose transcriptome correlates to the genes included in the clusters identified in Figure 4. (C) Top twenty kinase perturbations from Kinase Perturbation - GEO down database in Enrichr tool whose downregulated genes correlate to the genes included in the clusters identified in Figure 4. (D) Top twenty kinase perturbations from Kinase Perturbation - GEO up database in Enrichr tool whose upregulated genes correlate to the genes included in the clusters identified in Figure 4.
Supplementary Figure 9.
GO terms enriched in clusters of differentially expressed genes
(A) REVIGO plot of GO terms enriched in clusters 1 and 2, (B) clusters 3 and 4, (C) clusters 4 and 5 and (D) clusters 7 and 8. Clusters were identified in Figure 4.
Supplementary Figure 10.
AKT activity in MDCKI and MDCKI + bPAC cells
(A) Immunoblots of AKT activity (measured with Phospho-AKT antibody) of MDCKI + bPAC cells after 20min of light treatment with different duty cycles. Alpha-Tubulin (Tub.) was used for normalization. (B) Quantification of immunoblots in (A)
Supplementary Table 1. List of differential expressed genes and their adjusted p-value, log fold change and cluster identity. Related to Figure 4.
Supplementary Table 2. List of primers used for qPCR. Related to Supplementary Figure 7.
Supplementary Information. Description of computational model and parameters used in simulations. Related to Figure 3.
Acknowledgements
We thank Leonardo Morsut for the blue light LEDs and controllers; the El-Samad lab members, Marisa Rosa, Jeremy Chang, Zara Weinberg and Jeremy Reiter for critical feedback on the manuscript. This work was supported by the National Science Foundation through grant NSF-MCB 1715108 to HES