The stability of marine sectors of the Antarctic Ice Sheet (AIS) in a warming climate has been identified as the largest source of uncertainty in projections of future sea-level rise. Sea-level fall near the grounding line of a retreating marine ice sheet has a stabilizing influence on the ice sheets, and previous studies have established the importance of this feedback on ice age AIS evolution. Here we use a coupled ice sheet–sea-level model to investigate the impact of the feedback mechanism on future AIS retreat over centennial and millennial timescales for a range of emission scenarios. We show that the combination of bedrock uplift and sea-surface drop associated with ice-sheet retreat significantly reduces AIS mass loss relative to a simulation without these effects included. Sensitivity analyses show that the stabilization tends to be greatest for lower emission scenarios and Earth models characterized by a thin elastic lithosphere and low-viscosity upper mantle, as is the case for West Antarctica. The retreat of the Antarctic Ice Sheet and its potential contribution to future sea-level rise, is a major focus of climate research. Here, the authors show that positive feedbacks involving bedrock uplift and sea surface drop, may significantly impact the timing and extent of local ice-sheet retreat. The Antarctic Ice Sheet (AIS) has the potential to make a significant contribution to future sea-level rise2347Here we apply the most advanced type of these models11We use a coupled ice sheet–sea-level model2 levels in the atmosphere are linearly increased instantaneously  or over 1 kyr  at the start of the model run to two, four or eight times modern levels. This warming is applied by averaging together the appropriate RCM results for modern climatology and the various fixed, elevated atmospheric CO2 levels. Ocean temperatures are linearly, uniformly increased by 2 °C above modern climatology over the same time interval (see Methods). The black line in 2 levels are increased instantaneously and no sea-level feedback is included; that is, the bedrock and sea surface height elevations in the ice-sheet model are fixed in time. The solid red and blue lines represent simulations that adopt an Earth model (henceforth model HV for ‘high viscosity') with lithospheric thickness of 120 km, and upper and lower mantle viscosities of 5 × 1020 and 5 × 1021 Pa s, respectively, in the sea-level calculations. This model is representative of a class of viscoelastic Earth models that satisfy a range of globally distributed ice age data sets  of 3.8 m  led to changes in timing of significant retreat in the Amundsen Sea Embayment  from the base of the lithosphere to 200 km depth, and viscosities of 2 × 1020 and 3 × 1021 Pa s in the rest of the upper mantle and in the lower mantle, respectively. , 1 m less for the 4 × CO2 scenario and 4.3 m less for the 8 × CO2 scenario.Adopting the LVZ model increases the sea-level stabilization and reduces the predicted rise in GSLE for all emission scenarios as compared with the HV model, as viscous uplift takes place faster and deformation is more localized to the grounding line  is higher in amplitude and more localized near the grounding line in the LVZ model simulation relative to the HV case. However, in this case, the climate forcing is sufficiently strong that the ice volume plateaus after ∼1.5 kyr for both the LVZ and HV simulations rather than increasing . Note that in this high-emission scenario, significant retreat occurs in marine sectors of the EAIS, where the HV model may represent the Earth structure better than the LVZ model. We show the contributions from East and West Antarctica to the total Antarctic ice loss plotted in In the 8 × CO 0.6 kyr . As in tThe glacial isostatic adjustment model predicts changes in sea level associated with rotational, gravitational and Earth deformational effects. Self-gravitation in the (ice plus ocean) surface mass load and crustal deformation dominate the sea-level signal in the vicinity of an evolving ice sheet. These signals are coupled as self-gravitation influences the ocean load, which in turn drives crustal deformation. To investigate the importance of self-gravitation in our simulations, in 2 levels (3b), the model that incorporates self-gravitation in the surface mass load yields 60% of the grounded ice loss predicted in the simulation that includes full gravitational and deformational effects in the sea-level calculator. The relative importance of self-gravitation in these coupled ice sheet–sea-level simulations will depend on many factors, including the strength of the climate warming  global topography combined with Bedmap2 bedrock elevation over AntarcticaTo begin the initialization process, the ice-sheet model alone with no sea-level coupling is spun up to a modern equilibrium state forced with climatological ALBMAP atmospheric conditions2 values of 2, 4 or 8 × preindustrial CO2 level (280 parts per million by volume (p.p.m.v.)). The RegCM3 RCM2 scenario is forced by a climate model simulation, in which marine sectors of the West Antarctic have been removed, so that the ice-sheet elevation used in the RCM better captures the ice elevations predicted in the ice-sheet model. In future work, we will use asynchronous or transient climate models and representative concentration pathway (RCP) scenariosStarting from the equilibrated state for each Earth model, we perform a suite of 5-kyr-long coupled model simulations, applying a simple, linear ramp up of climate forcing, varying the length of the ramp from 1 to 3 kyr. For the atmosphere, the warming is applied as an anomaly to modern climatology. The anomaly is the difference in modelled atmospheric temperature (additive) and precipitation (multiplicative) between a modern control simulation and simulations for fixed CO2) experiments here produce substantial EAIS margin retreat .Supplementary Figures 1-6


Above- and belowground carbon (C) stores of terrestrial ecosystems are vulnerable to environmental change. Ecosystem C balances in response to environmental changes have been quantified at individual sites, but the magnitudes and directions of these responses along environmental gradients remain uncertain. Here we show the responses of ecosystem C to 8–12 years of experimental drought and night-time warming across an aridity gradient spanning seven European shrublands using indices of C assimilation (aboveground net primary production: aNPP) and soil C efflux (soil respiration: Rs). The changes of aNPP and Rs in response to drought indicated that wet systems had an overall risk of increased loss of C but drier systems did not. Warming had no consistent effect on aNPP across the climate gradient, but suppressed Rs more at the drier sites. Our findings suggest that above- and belowground C fluxes can decouple, and provide no evidence of acclimation to environmental change at a decadal timescale. aNPP and Rs especially differed in their sensitivity to drought and warming, with belowground processes being more sensitive to environmental change. Belowground autotrophic and heterotrophic processes can result in C loss from terrestrial systems and feed back to the atmosphere. Rates of above and belowground C turnover have been linked to soil hydraulic properties38The fifth IPCC report highlighted the link between atmospheric C dioxide CO concentrWe report on the long-term  responses of related aNPP and Rs measurements to seasonal reductions of 8–49% mean annual precipitation (MAP) and year-round increases of 0.2–0.9 °C air temperature (MAT), and the impact of these responses on C assimilation and soil C efflux. Sites ranged from xeric to mesic  and hydric . The common experimental manipulation setup across sitesSite-specific temporal responses of aNPP and Rs to drought and warming varied greatly across sites . All sitp = 0.008, p = 0.001, The effects of precipitation and its legacy effect on aNPP have been explored for arid and sub-humid ecosystems2p = 0.378, 14p = 0.029, In contrast to drought, experimental moderate warming did not change aNPP across sites compared to their controls . The effects of drought on aNPP are superimposed on site-specific interannual variability in precipitation; this could have made any subtle pattern in the responses of aNPP more difficult to detect. In contrast to aNPP, the Rs response changed across the Gaussen gradient, with a tendency of suppressed Rs increasing from xeric to mesic sites (p = 0.131), excluding the hydric UK site. The hydric site is seasonally water logged and a drought-induced decrease in soil moisture is stimulating plant and microbial activity; this causes an opposite Rs response compared to xeric and mesic sites where soil water availability is limiting activity.We further investigated whether the observed responses of aNPP and Rs were affected by MAP and MAT. We used the position of each site on the Gaussen index, from xeric sites to hydric sites, to describe site-level climate . The degp = 0.342) and had a small, negative effect on Rs (p = 0.036). The magnitude of this warming effect decreased from xeric to mesic sites which is in agreement with recent findings that soil C stocks decrease under warming in high-latitude areas9We expected warming to increase aNPPThe drought and warming responses of Rs compared to the controls were more consistent over time than aNPP responses . A recen204The differential response of Rs to drought and warming at the non-hydric sites (inhibiting Rs) compared to the hydric UK site (stimulating Rs) can be explained by the location of the sites on an Rs-soil moisture response envelope . A soil The DK-B site was the only site where Rs responded differently to both drought and warming compared to the control . The conWe argue that although the use of the Gaussen index, combining MAP and MAT is very useful for demonstrating broad trends of ecosystem responses to climate change, site-specific characteristics like physical properties and soil moisture status are critical to aid understanding as these underpin important processes that are influenced by environmental change. Recent research shows that drought-induced alteration of the soil structure can cause a major shift in soil moisture behaviourMoreover, soil water availability is important because it affects plant and microbial processesA change in terrestrial C turnover caused by environmental change or other factors will feed back to the atmosphereINCREASE (Integrated Network on Climate Research Activities in Shrubland Ecosystems) is a network of climate change experiments in shrublands spanning a cross-continental gradient in precipitation and temperature−2) was calculated from an annual point intercept surveyAGB (g biomass m−2 yr−1) was measured using litter collectors. Dependent on the site, 5–30 litter pots (1.5–4.4 cm diameter) were placed randomly beneath the canopy in each plot. At the DK sites, grass litter was estimated based on pin-point measurements. Litter was collected monthly or every 2 or 6 months (site dependent), dried to constant weight at 60 °C and weighed. Where AGB was present for a site but litterfall was not measured separately, average litterfall of all measurements was used instead.Litterfall (g biomass m−2 yr−1) is a combination of newly developed photosynthetically active leaves and the (woody) growth increment. aNPP was calculated based on the annual AGB increment and litterfall  where the AGB increment is the difference in AGB between following years. If the AGB increment was positive, aNPP was the sum of the increment plus the litterfall of the current year. If the AGB increment was negative then:Aboveground net primary production  and aNPP (g biomass m−2 yr−1) were converted to g C by multiplying by 0.5 .where year was the year of interest and year-1 was the measure of the previous year. If litterfall data was not available for a particular year of AGB measurement, the average litterfall of the site was used instead. aNPP was calculated differently in HU and UK due to the site-specific character of aNPP. In HU, aNPP was similar to the litterfall of the same year as plants were deciduous and shed their leaves before winter. In the UK, aNPP was similar to the litterfall of the previous year as it was the best predictor for aNPP at the site due to the perennial character of the dominant plant Rs was measured in three permanent soil collars (10 cm diameter) placed in each plot. All aboveground vegetation was removed from the inside of these collars. Rs was measured biweekly (DK-B) or monthly throughout the year  or estimated based on campaign measurements (SP). Rs was up-scaled to annual Rs92425p-values below 0.05 show a significant treatment effect on AGB, aNPP or Rs .Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


The notion that small changes can have large consequences in the climate or ecosystems has become popular as the concept of tipping points. Typically, tipping points are thought to arise from a loss of stability of an equilibrium when external conditions are slowly varied. However, this appealingly simple view puts us on the wrong foot for understanding a range of abrupt transitions in the climate or ecosystems because complex environmental systems are never in equilibrium. In particular, they are forced by diurnal variations, the seasons, Milankovitch cycles and internal climate oscillations. Here we show how abrupt and sometimes even irreversible change may be evoked by even small shifts in the amplitude or time scale of such environmental oscillations. By using model simulations and reconciling evidence from previous studies we illustrate how these phenomena can be relevant for ecosystems and elements of the climate system including terrestrial ecosystems, Arctic sea ice and monsoons. Although the systems we address are very different and span a broad range of time scales, the phenomena can be understood in a common framework that can help clarify and unify the interpretation of abrupt shifts in the Earth system. If the feedback is very strong, alternative stable states can exist under the same external conditions , varies monotonically and gradually, so that the system stays close to a stable equilibrium Such oscillations will affect parts of the climate system or ecosystems that have intrinsic thresholds or bifurcations: a gradual change in the mean, amplitude or frequency of periodic drivers can trigger abrupt change in these systems. In the following, we first illustrate these phenomena with a simple mathematical system. Subsequently, we point out how models and observations indicate the existence of these phenomena in many elements of the Earth system, spanning a vast range of time scales. We illustrate some consequences of this view for monsoon systems, sea ice and terrestrial ecosystems, and argue how a general view on tipping points can contribute to a conceptual understanding of different tipping elements in the Earth system.To understand the occurrence of tipping points in periodically forced systems, we take a very simple dynamical system as a useful conceptual model example, where the evolution of state x over time t in this system is determined byThe black lines in Fig. m with amplitude Da and period T.We now investigate the behavior of the system in response to a driver that oscillates around a mean Dm, Da and T, until it reaches a stable attractor in form of a periodic oscillation. Like the driver, the system’s solution is periodic in time with period T 18. Figure m 21. This stands in contrast to the fact that the climate system is interspersed with a number of internal and external oscillations that may cause such transitions on a vast range of time scales. This seems plausible because hardly any oscillation remains the same over time. For example, the seasonal cycle of solar insolation varies due to the Earth’s precession, which in turn is modulated by eccentricity. Similarly, the diurnal insolation cycle depends on the seasons, the Earth’s orbital parameters and its rotation rate. Moreover, modes of internal climate variability often have a distinct range of frequencies, and affect ecosystems and other components of the climate system. Besides these naturally occurring phenomena, the human interference with the climate system is another reason why such oscillations might change. For example, a human impact on the amplitude and frequency of ENSO can be expected22. Even the amplitude and timing of the annual cycle can change in many variables when the balance of climate feedbacks shifts under increased greenhouse gas concentrations, for example associated with changes in Arctic cloud cover, or the loss of sea ice23. Figure Mathematically, bifurcations in time-independent and periodically forced systems are equivalent25. For example, it has lately been found that the structure of the nocturnal atmospheric boundary layer over land can show two alternative regimes27. The question which regime will be established can crucially depend on the conditions during the evening28. On a much larger scale, marine boundary layer clouds show distinct regimes in observations and multiple equilibria in specific models30. It therefore seems rewarding to study how the diurnal cycle of solar insolation32 or the annual cycle of sea surface temperatures can affect the long-term structure of the lower atmosphere.Starting our overview with small scales, tipping point phenomena have a long tradition in the nonlinear world of fluid dynamics33, marine sea-grass ecosystems34, tropical forests, and drylands35. In terrestrial ecosystems, rainfall is often the most important environmental driver, and the longevity and severance of droughts has long-term consequences. We will discuss the cases of monsoons, sea ice and terrestrial ecosystems in more detail below.Particularly important for the lives of many people are the abrupt shifts in monsoon systems occurring within the seasonal and orbital cycles. Similarly, the current loss of summer sea ice reminds us that sea ice is nonlinear system that is subject to a marked seasonal cycle. Moreover, multiple equilibria are supposed to exist in several marine and terrestrial ecosystems including lakes36. Probably related to the AMOC are the quasi-regular Dansgaard-Oeschger events which have been argued to be caused by a regular driver37. A subject where the role of periodic drivers has been discussed explicitly are glacial-interglacial cycles. In particular, it has been shown that they can conceptually be understood as forced shifts in a multi-stable landscape38. Due to the inertia of the cryosphere and lithosphere, the climate is not in equilibrium with the orbital forcing. Small insolation differences from one orbital cycle to the next can thus cause very different climates40. Such a conceptual view can also be beneficial for understanding other components of the Earth system and can inspire research questions. We will illustrate this by focusing on three example cases.Finally, an iconic example of a tipping point is the Atlantic Meridional Overturning Circulation (AMOC) which can show multiple states in models of different complexity41. Due to geographical differences between the monsoon regions, each monsoon system is usually studied on its own. However, observations from several monsoon systems show an intriguing asymmetry in the annual cycle of precipitation: while the onset of the monsoon rainfall in spring tends to occur rather abruptly, its retreat in autumn occurs much more gradually. Abrupt monsoon onsets have been observed in West Africa42, the southwest Indian coast43, and in East Asia, where several abrupt stages of monsoon onset have been identified44. This behavior stands in contrast to the gradual and symmetric annual cycle of solar isolation that drives the monsoon evolution.Monsoon systems cover a large part of the subtropical land areas and supply many people with water. The most important monsoons occur in India, East Asia, West Africa, Australia and North and South America. The classical view of monsoon systems is that subtropical heat lows in summer induce a low-level circulation from the tropical ocean towards higher latitudes that supplies the subtropical land with moisture46. In particular, the mechanism has been found to operate in a regional model of West African monsoon climate47 and is supported by observations48. Second, air-sea interactions involving wind, evaporation, clouds and moisture advection can provide positive feedbacks which can rapidly enhance the monsoon after its onset50. One important positive feedback results from the advection of moisture by the low-level monsoon flow. When this moisture condenses over land, the latent heat release further enhances the circulation. This feedback is at the heart of the monsoon model by51 that produces abrupt monsoon shifts, and whose simplified version8 we will apply below. Third, the distribution of land surface properties such as albedo and orography modulate the monsoon onset54. Fourth, the seasonal cycle is superimposed by faster oscillations, causing periods of rapid change55. Such oscillations can make the monsoon onset more abrupt because they tend to be phase-locked to the annual cycle57.There are many potential reasons for abrupt monsoon onsets. First, meteorological theory predicts that switches from a zonal to a meridional flow can occur due to hydrodynamic instabilities when the atmosphere is sufficiently heated away from the equatorτ/T, similar to the example system in Fig. 13. These switches occur due to the oscillations of the Earth’s orbit that modulate summer insolation on the northern hemisphere. In this regard, the behavior of monsoons within a year and on timescales of hundred thousands of years is remarkably similar.Despite the complexity and geographical differences between the monsoon systems, the widespread existence of sudden monsoon onsets invites describing them within the common concept of tipping points. In addition, the annual cycle of solar insolation is a cyclic driver of these systems. One could therefore speculate that monsoons are periodically driven bistable systems. As the monsoon circulation adjusts very quickly to its boundary conditions it would be best described as a system with a very small ratio et al.8 which describes an idealized summer monsoon that can switch between an “on” and “off” state (see Supplementary Information). The mechanism responsible for the abrupt behavior in the model is the moisture-advection feedback. It has recently been challenged that this feedback would be strong enough to support such nonlinearity60. While the underlying mechanism is hence uncertain, it is clear that abrupt monsoon switches both in the seasonal cycle and in response to orbital forcing do occur. By applying the tipping point framework we speculate that a common mechanism is behind the jumps within the annual cycle and the abrupt transitions on millennial time scales. While the Levermann model suggests the moisture-advection feedback as the cause, any other positive feedback would essentially yield the same qualitative behaviour.To conceptualise the link between abrupt monsoon transitions on different time scales we use the simple model of Levermann, o and the radiative balance of the atmospheric column over land R. Without any alterations to the model we generalize R here to also include the sensible heat flux at the Earth’s surface and call the resulting quantity R*. Figure o and R*. The threshold that separates the “on” and “off” states can be seen as a sharp boundary between the blue area  and other colors .The most important parameters that determine the model’s state are the atmospheric humidity over the ocean qo and R*. The model is only valid for summer conditions and cannot describe any precipitation not associated with the summer monsoon. We therefore set P to zero in other seasons, knowing that no monsoon rainfall occurs during that time. In order to obtain an asymmetric annual cycle with an abrupt onset like in observations, we apply two strategies:9 by introducing a small bistable regime at the critical threshold (the shaded region with a width of 0.5 g/kg at the boundary).We follow the approach byo lag the seasonal cycle in R*. This approach may be justified by the different memories of ocean and atmosphere: while the atmosphere can adjust to the solar insolation within days, the oceanic temperature which controls specific humidity lags the insolation cycle by many weeks. The resulting annual cycle of qo and R* is shown as a red orbit in the parameter plane of Fig. We introduce a phase lag of one month between the two cyclic drivers, making oceanic moisture qWe now impose an annual cycle on the parameters qo and R* is shown in the inset of Fig. o decline and rainfall decreases with them, until it drops to zero when the “on” state disappears at the left border of the shaded bistable region. An alternative situation with a different mean of qo and R* is shown by the dashed orbit and dashed annual precipitation cycle, where precipitation is zero during the whole year. The transition from the regime where a monsoon circulation establishes each year to the regime where it does not establish constitutes a generalized tipping point. The monsoon collapse occurs when the threshold value of qo and R* is not reached anymore in summer.The annual cycle of rainfall that results from the trajectory of q61. The most important argument for this idea is the positive feedback between surface albedo and sea-ice loss . It has been found that the existence of bifurcations in sea ice models depends on the description of spatial differences and the seasonal cycle63. While future ice loss is reversible in comprehensive models, these models also indicate that Arctic winter sea ice can be very sensitive to warming64. Moreover, a comprehensive model shows three alternative stable sea-ice states on a completely land-free Earth66. One of these states is a globally ice-covered planet, resembling the “snowball” conditions that occurred during the Neoproterozoic. This state also occurs in at least one other complex model under present-day boundary conditions67.It has been hypothesized whether global warming can cause abrupt and irreversible sea-ice loss in the Arctic68 provides an insightful parameter analysis of a simple Arctic sea ice model that is driven by the annual cycle of solar and terrestrial radiation. Small variations in these fluxes can lead to abrupt changes in the annual cycle of sea ice. Compared to the previous systems, Arctic sea ice and the surface ocean constitute a slow system that is not in equilibrium with the annual cycle of insolation. This can be seen from the fact that the annual cycle of Arctic sea ice volume lags the insolation cycle by approx. three months. The analysis by Eisenman68 was performed with a model that already included the annual cycle of short- and long-wave insolation. The concept of tipping points in periodically forced systems suggests that the bifurcations in the model originate from multiple states under certain fixed values of insolation, as the following model results show.Hence, it is plausible that tipping points in sea ice have occurred in the Earth’s past, triggered by changes in the continental configuration, Milankovitch forcing, or the seasonal insolation cycle. In this regard, Eisenman68 calculates an energy balance for a well-mixed box of ocean water, covered with ice of a single thickness (see Supplementary Information). When ice is present, the enthalpy E is negative and proportional to the ice thickness. In the absence of ice, E is positive and proportional to the ocean temperature. The bifurcation parameter Lm represents the annual mean outgoing long-wave radiation budget at the surface (not including temperature feedbacks) and becomes smaller when the climate warms. For values of Lm between approx. 65 W/m2 (which roughly represents the present-day climate) and 50 W/m2, two stable solutions can be found: a seasonally ice-covered ocean and an ice-free ocean . In addition, we show to what state the sea-ice system would evolve if there was no annual cycle (green and blue lines). To this end, we fix S and L to the values corresponding to a particular day in a year and integrate the model forward in time, starting from large positive and negative E. For most days in the year, only one asymptotic solution exists. For example, under permanent summer conditions, all ice melts away and the ocean becomes so warm that the outgoing long-wave radiation balances the absorbed radiation . As the ice-ocean system cannot fully adjust to the fast insolation changes in the course of a year, the amplitude of its annual cycle is very small compared to the difference of its steady state solutions. The solutions overlap only during a short period in spring and an even shorter period in autumn, in the rest of the year only one solution exists. Nonetheless, the existence of multiple solutions still carries over to the periodically forced system for a certain parameter regime.The solutions in Fig. m. In a cold climate, the summer is too short and insolation is too low to melt enough ice within one summer; a perennial ice cover is therefore the only solution of the periodically forced system. Under global warming (decreasing Lm), the asymptotic solution moves to larger E and the melting season becomes longer  alone. As Eisenman’s single column model above shows, the existence of multiple equilibria under fixed seasonal conditions in a model is a ‘smoking gun’ pointing to a strong positive feedback. Such a feedback indicates that the system is prone to tipping points even if the multistability is not apparent in the seasonally forced (non-autonomous) system. Changes in the insolation forcing  can hence bring the system into a regime where abrupt changes are possible. The same can occur due to changes in the intrinsic relaxation time of the system, for instance related to the fact that the growth rate of sea ice depends on its thickness and hence the background climate70. Second, abrupt shifts can also occur in systems without multiple states. Specifically, the freezing point of water acts as a natural threshold for the formation of sea ice, making ice cover very sensitive on temperature in a certain regime and prone to reversible abrupt shifts64. The quasi-periodic changes in insolation related to Milankovitch forcing might therefore switch the Arctic back and forth between an ice-free and a seasonal ice regime . A similar switch-mechanism determining the growth and melt of ice sheets has also been discussed as an explanation of ice ages during the Pleistocene71.In conclusion, the fact that a certain comprehensive model does not show multiple solutions under the same external conditions does not automatically suggest that tipping points are not possible. This has two reasons: First, tipping points can remain hidden in a parameter range related to periodic forces that is not explored when varying one parameter . Tropical SSTs show characteristic modes of variability like the El Nino Southern Oscillation (ENSO), Pacific decadal oscillation (PDO) and Atlantic multidecadal oscillation (AMO) which affect tropical rainfall. For example, high Atlantic SSTs can lead to severe droughts in the Amazon like those observed in recent years83. It has therefore been investigated if these feedbacks can give rise to multiple equilibria and whether the end of the Green Sahara some 5,500 years ago can be interpreted as a tipping point84. Moreover, rainfall in Northern Africa shows large decadal variations. Climate models show that these variations are caused by SST changes in several ocean basins and that the Sahel drought of the 1970s and early 1980s was caused by such modes of natural climate variability86. The time scale of these inter-annual fluctuations is of similar magnitude than the time scale of responses in grass coverage in such drylands. It is therefore plausible that rainfall fluctuations can lead to locally amplified shifts between a desert and a grass-covered state 24 and bifurcations in ordinary differential equations is not straightforward25, and neither is such theoretical knowledge well applied to the specific physical systems mentioned above. Nonetheless, nonlinearities and critical thresholds are arguably present in all Earth system components, providing a basis for abrupt shifts. At the same time, other yet unrecognized tipping points could exist in the Earth system.For the sake of simplicity, our example models are a very idealized representation of reality. For example, alternative equilibria are arguably less likely or at least harder to find in more complex models90. In this regard, the concept of tipping points triggered by changes in periodic forcings is one representation of the fundamental question how vulnerable components of the Earth system are to environmental change. Theoretical progress has been made in recent decades to generalise concepts from bifurcation theory, for example by defining pullback attractors for deterministic non-autonomous systems91, and random attractors for stochastic systems93. Unfortunately, such theoretical insights do not always feed back to our knowledge on tipping points in the Earth system.In more realistic systems the drivers also have a more continuous spectrum. In models, the fast time scales in such spectra are often described by stochastic terms. The amplitude and period of the periodic oscillations we assumed above can be seen as an analogy for the typical magnitude and duration of random external fluctuations. Small changes in these statistical properties can lead to large and sometimes counter-intuitive changes in the distribution of a system’s state96. In the case of the systems above, classical indicators like rising variance and autocorrelation are not necessarily the most useful approach97. It is therefore important to explore alternative approaches to early warnings of abrupt change. Although conceptual models cannot replace studies based on observations and process-based modelling, they can help raise questions for future research. For example, it may be beneficial to compare abrupt monsoon changes on different time scales and in different regions: Are there common mechanisms behind the abrupt onsets in different monsoon systems on the planet? Are these mechanisms also responsible for abrupt monsoon shifts in palaeorecords? Is the expectation to see early warning signals before the occurrence of a certain shift supported by current process understanding? Will stronger El Niño events have irreversible consequences for tropical forest and savannah ecosystems? Was sea ice involved in a tipping point in the Earth’s history, and what was the role of periodic forcings? To explore such questions, it would be beneficial to further develop models of low to intermediate complexity in addition to existing Earth system models. Such a model hierarchy is most established in the case of sea ice models, but patchy in the case of ecosystems, and lacking in the case of monsoons. A suite of models that are easy to use would also better connect research attempts to understand different monsoon systems.One particular challenge in this regard is to investigate the applicability of statistical stability indicators that could provide an early warning of abrupt change. In climate research, this concept has mostly been explored in the static context of classical tipping points98, and transitions between vegetation states depend on the global background climate involving the ice sheets84 and the Atlantic overturning circulation99. In light of the large uncertainties, the candidates and suspected mechanisms of tipping points will certainly evolve with new knowledge. As the Earth system is interspersed with feedbacks and natural thresholds, and features oscillations and environmental fluctuations on a vast range of time scales, it seems plausible that non-equilibrium tipping points have already occurred in the past and could occur again in the future.On top of that, studies on isolated tipping elements may even not be sufficient, as different Earth system components are linked in their stability behaviour and thus do not act in isolation. For example, sea-ice loss and the meridional ocean circulation affect each otherAll analysed data can be produced with the simple models as explained in the Supplementary Information, and can also be obtained from the lead author (S.B.).Supplementary Information


L. Importantly, phosphorylation of DAPK2 by AMPK enhances DAPK2’s ability to phosphorylate Beclin-1, and depletion of DAPK2 reduces autophagy in response to AMPK activation. Our study reveals a unique calmodulin-independent mechanism for DAPK2 activation, critical to its function as a novel downstream effector of AMPK in autophagy.Autophagy is an intracellular degradation process essential for adaptation to metabolic stress. DAPK2 is a calmodulin-regulated protein kinase, which has been implicated in autophagy regulation, though the mechanism is unclear. Here, we show that the central metabolic sensor, AMPK, phosphorylates DAPK2 at a critical site in the protein structure, between the catalytic and the calmodulin-binding domains. This phosphorylation activates DAPK2 by functionally mimicking calmodulin binding and mitigating an inhibitory autophosphorylation, providing a novel, alternative mechanism for DAPK2 activation during metabolic stress. In addition, we show that DAPK2 phosphorylates the core autophagic machinery protein, Beclin-1, leading to dissociation of its inhibitor, Bcl-X DAPK2 is a calmodulin-regulated protein kinase implicated in autophagy regulation, but how physiological stress leads to its activation is yet unknown. Here, the authors show that the central metabolic sensor AMPK phosphorylates DAPK2 to promote autophagy in a calmodulin-independent mechanism. Autophagy is involved in many cellular events, including development, immune defense, programmed cell death, and neuronal degeneration3, and is a physiological response to fasting. Under steady-state conditions, basal levels of autophagy are maintained within the cell. Under certain stress conditions, such as nutrient deprivation, autophagy is stimulated in order to ensure cell survival by providing the components needed to maintain homeostasis4. The first step in autophagosome biogenesis, termed the nucleation step, is mediated by a class III PI(3)K multi-protein complex containing Beclin-1, Atg14, Vps15, and the lipid kinase Vps346. This complex is responsible for the production of PI(3)P at the autophagosome assembly site, which in turn serves to recruit other autophagic proteins. The autophagy-promoting activity of Beclin-1 is suppressed, among other mechanisms, by direct binding of anti-apoptotic members of the Bcl-2 family to the BH3 domain of Beclin-17.Autophagy is an evolutionarily conserved, highly regulated intracellular bulk degradation process in which a double-membrane vesicle, called an autophagosome, is formed in the cytosol and encloses cytosolic material. The autophagosome later fuses with the lysosome, where its content is degraded -regulated Ser/Thr kinase8 and a member of the DAPK family (for a review see ref. 9). The founder member of the family is DAPK1, a large multi-domain Ca2+/CaM-regulated Ser/Thr kinase implicated in a wide range of biological activities (for a review see ref. 10). Both DAPK1 and DAPK2 show a high degree of homology in their N-terminal catalytic domains and the adjacent CaM auto-regulatory domains. Yet they differ substantially in the structure of their extra-catalytic domains, resulting in different intracellular localizations and different profiles of interacting proteins10. In DAPK2, a short C-terminal tail of 40 amino acids follows the kinase and the CaM auto-regulatory domains. The crystal structure of DAPK2 appears as a homodimer in an auto-inhibited conformation, blocking substrate access11. Homodimerization is mediated by a basic loop in the catalytic domain11, which is a signature of the DAPK family, and by the C-terminal tail12.Death-associated protein kinase 2  is a 42-kDa Ca13), including myeloid differentiation14, erythroblast loss during erythropoiesis15, granulocyte motility16, and anoikis17. Another prominent function of DAPK2, which it shares with DAPK1, is its link to autophagy, initially discovered in overexpression experiments, in which DAPK2 triggered the appearance of double-membrane autophagic vesicles18. Subsequent knock-down experiments confirmed the involvement of DAPK2 in regulating autophagy in human cell lines19 and preadipocytes20.DAPK2 mediates a range of cellular processes , which constitutes a 14-3-3 binding site. Phosphorylation and consequent 14-3-3 binding inhibit DAPK2 activity21. The kinase(s) responsible for this inhibitory phosphorylation are yet unknown, though AKT was proposed as a possible candidate26.Consistent with its various functional roles, it is not surprising that DAPK2 activity is tightly regulated in cells. In the absence of CaM, the auto-regulatory domain binds to the catalytic cleft, blocking access of exogenous substrates and inhibiting DAPK2 function. Autophosphorylation of Ser308 within the CaM auto-regulatory domain reinforces this inhibitory mechanism by stabilizing the docking of the CaM auto-regulatory domain in the catalytic cleft27. Once activated, it acts to suppress anabolic pathways and promote catabolic pathways, such as autophagy. We show that AMPK phosphorylates DAPK2 on Ser289 in cell cultures exposed to metabolic stress and in muscle tissue of fasted mice. Most interestingly, we demonstrate that this single phosphorylation of Ser289, localized at a critical site in the DAPK2 structure, provides a novel non-canonical mechanism of DAPK2 activation that is independent of CaM binding. It mitigates the Ser308 auto-inhibitory phosphorylation and reduces DAPK2 homodimerization, two activating events that most likely result from a conformational change imposed by Ser289 phosphorylation. In addition, we show that DAPK2 phosphorylates the core autophagic machinery protein, Beclin-1, on Thr119 located in its BH3 domain, thus causing it to dissociate from its inhibitor Bcl-XL. The importance of this novel AMPK–DAPK2 axis in autophagy regulation is further substantiated by the findings that phosphorylation of DAPK2 by AMPK enhances DAPK2’s ability to phosphorylate Beclin-1, and that depletion of DAPK2 reduces autophagy in response to AMPK activation. Altogether, these results suggest that DAPK2 is an important downstream effector of AMPK in promoting autophagy.In this study, we identify the metabolic sensor AMP-activated protein kinase (AMPK) as an upstream activator of DAPK2. AMPK is a key regulator of energy homeostasis in eukaryotic cells28 at high stringency, looking for all potential motifs. Four potential phosphorylation sites were found . Next, this list was crossed with the PhosphoSitePlus database29 for reported phosphorylation sites on DAPK2. Two sites were found in both lists: Ser289 and Thr359. According to the Scansite algorithm, both Ser289 and Thr359 fit the consensus sequence of AMPK, and Ser289 also fits the consensus sequence of PKA. We focused on AMPK as a possible upstream regulator of DAPK2, as both AMPK and DAPK2 are known to promote autophagy. Therefore, we hypothesized that they may act together through a signaling cascade.To search for possible kinases that regulate DAPK2, the amino acid sequence of DAPK2 was analyzed using the Scansite algorithmTo test if AMPK can directly phosphorylate DAPK2, we performed an in vitro kinase assay using recombinant proteins and analyzed it by mass spectrometry for phospho-peptide mapping. The analysis revealed that DAPK2 was indeed phosphorylated by AMPK, and that the only site that was phosphorylated at high confidence was Ser289 Fig. . To furt30), Ser289, as well as the adjacent residues that constitute the consensus sequence recognized by AMPK, were found to be highly conserved  and DAPK2 Ser289 phosphorylation Fig. . The spe33. Indeed, increasing calcium levels in A549 cells with ionomycin caused both AMPK activation and DAPK2 Ser289 phosphorylation . To test if the phospho-mimicking mutation has the same effect as actual phosphorylation by AMPK, FLAG-DAPK2 WT and Ser289 mutants were immunoprecipitated from HEK293T cells and used in an in vitro kinase assay on recombinant MLC. Notably, the Ser289 phospho-mimicking mutant showed significantly higher catalytic activity towards MLC Fig. , indicat23. Thus the blebbing phenotype can serve as a quantitative measure to assess DAPK2 catalytic activity in cells. HEK293T cells were transfected with either DAPK2 WT or Ser289 mutants, or empty vector as control, together with GFP, and cell morphology was examined by fluorescent microscopy , upon the addition of Ca2+/CaM the stimulatory effect of the mutation was lost . Therefore, not only is the S289D mutant highly active without the presence of Ca2+/CaM, but CaM binding also has a considerably reduced effect on its catalytic activity.DAPK2 is a CaM-regulated kinase and is thus activated by calcium signaling in cells. On the other hand, Ser289 phosphorylation is induced by metabolic stress, which is not accompanied by elevation in calcium levels. It was therefore interesting to compare the two mechanisms of activation and test whether they are synergistic or mutually exclusive. To this end, an in vitro kinase assay was performed using the Ser289 mutants under two different conditions: with the addition of Caost Fig. . Thus, tNext, the effect of Ser289 phosphorylation on DAPK2’s affinity to CaM was tested by ELISA. Wells were coated with CaM and then incubated with equal amounts of DAPK2 WT and Ser289 mutants. A DAPK2 mutant lacking the entire CaM auto-regulatory domain (Δ73 mutant) was used as a negative control. Remarkably, the S289D phospho-mimicking mutant gave rise to a greatly reduced signal compared to DAPK2 WT (~5 fold lower) Fig. , implyin12. The finding that the S289D mutant does not depend on CaM for activation prompted us to test whether Ser289 phosphorylation may functionally mimic the effect of CaM binding and oppose the effect of Ser308 phosphorylation. To this end, HEK293T cells were transfected with an S308D or an S289D S308D double mutant, together with GFP. Upon overexpression, the S308D phosphomimetic mutant was completely silent and did not induce any membrane blebbing  can be detected, representing a dimer fraction that is resistant to the denaturing conditions of the SDS–PAGE12. Interestingly, the dimer was less abundant for the Ser289 phospho-mimicking mutants (S289D & S289E), but not the phospho-silencing mutant S289A, compared to WT DAPK2 . HEK293T cells were transfected with different combinations of L1 and L2-fused DAPK2 WT and Ser289 mutants. The luminescence signal represents dimerization of L1 and L2-fused DAPK2 monomers. The two Ser289 phospho-mimicking mutants yielded a significantly reduced signal in all combinations (mutant/mutant or WT/mutant), while the phospho-silencing mutant gave rise to luciferase complementation signals similar to the WT protein  luminescence signal compared to Beclin-1 WT . Notably, depletion of DAPK2 reduced the levels of lipidated LC3 Fig. , indicatcta Fig. . Altoget47. It is possible that Ser289 can be phosphorylated by several kinases. However, as phosphorylation by cGK-I was observed only in vitro, further work needs to be done in order to determine if it is indeed a regulator of DAPK2 in cells.As DAPK2 is involved in several critical cellular processes, including autophagy, its activity in cells is tightly regulated. The two known regulatory phosphorylations of DAPK2  are inhibitory. In this work, we have identified an activating phosphorylation event: phosphorylation of Ser289 by AMPK. We have confirmed that this phosphorylation occurs in response to AMPK activation in cells. Moreover, it occurs in vivo in mouse muscle tissues as part of the physiological response to fasting. This is the first time that DAPK2 was shown to be activated by metabolic stress. It should be noted, that a human protein microarray screen for cGK-I substrates identified DAPK2, and one of the phosphorylated residues found was Ser289DAPK2 activity is regulated by several inhibitory mechanisms. Until now, it was assumed that DAPK2 must undergo Ser308 dephosphorylation and bind CaM in order to become fully active. Interestingly, Ser289 phosphorylation activates DAPK2 without either Ser308 dephosphorylation, as observed using the S289D S308D double mutant, or CaM binding. Thus, Ser289 phosphorylation provides a novel, alternative mechanism for DAPK2 activation, independent of calcium signaling. We found that Ser289 phosphorylation functionally mimics two effects of CaM binding: reduction in Ser308 autophosphorylation and decreased homodimerization. Remarkably, Ser289 phosphorylation not only mimics the effects of CaM binding, but also reduces DAPK2’s affinity to CaM, thus comprising a completely separate regulatory pathway.11, Ser289 is located in a short loop (residues 289–292) right before the inhibitory CaM binding helix L, a key step in autophagy induction. Even though it has been quite some time since DAPK2 was first implicated in autophagy induction18, this is the first core autophagic machinery protein that was found to be a direct substrate of DAPK2. As very few substrates of DAPK2 have been identified so far, this finding is of additional significance. Interestingly, the same site on Beclin-1 was found to be phosphorylated by two additional kinases, DAPK144 and ROCK45, highlighting its importance as a hotspot in the regulation of the Beclin-1-Bcl-XL interaction.In this work, we have identified a new substrate of DAPK2, Beclin-1. Specifically, we have shown that DAPK2 phosphorylates Thr119 within the BH3 domain of Beclin-1, thus promoting dissociation of Beclin-1 from its inhibitor Bcl-X38. These major differences between the two kinases can be explained by the fact that DAPK1 and DAPK2 differ substantially outside of their kinase and CaM auto-regulatory domains9. DAPK1 (160 kDa) has many extra-catalytic regulatory and protein interacting domains that DAPK2 (42 kDa) lacks, such as a ROC-COR domain and a death domain. In contrast, DAPK2 has a unique 40 amino acid tail that was shown to have important distinct regulatory functions. Thus, although DAPK1 and DAPK2 may possess similar substrate preferences, such as Beclin-1, their regulation in cells by upstream modulators, and the spectrum of their interacting proteins, is expected to be quite different. Here, we show for the first time differential regulation of the two kinases by opposing triggers. These profound differences in the regulation of DAPK1 and DAPK2 are of great interest, and shed some light on the different roles that these similar kinases may serve in cells.The similar substrate preference of DAPK2 and DAPK1 stems from the ~80% sequence identity shared by their kinase domains. The CaM auto-regulatory domains are slightly less conserved, at 50% sequence identity. Although Ser289 is conserved in DAPK1, it is not phosphorylated upon metabolic stress-induced AMPK activation, but it is phosphorylated upon activation of the opposing MAPK/ERK survival signaling pathway. Moreover, in the context of cell surviving signaling, Ser289 phosphorylation of DAPK1 conversely inhibits its apoptotic activity in cells48. Moreover, activation of AMPK by metformin promotes Beclin-1-Bcl-2 dissociation, through activation of JNK-mediated phosphorylation of Bcl-249. The fact that AMPK promotes Beclin-1 activation through several different mechanisms, by direct phosphorylation as well as by activating other kinases, emphasizes the importance of this step in autophagy initiation.We have shown by siRNA knock-down experiments that depletion of DAPK2 reduces autophagy induction in response to AMPK activation. This indicates that DAPK2 is one of AMPK’s important downstream targets in promoting autophagy. Specifically, it suggests that DAPK2 Ser289 phosphorylation enhances its pro-autophagic activity in cells. Accordingly, in vitro phosphorylation of DAPK2 by AMPK enhanced DAPK2’s ability to phosphorylate Beclin-1. Interestingly, AMPK phosphorylates Beclin-1 directly on several sites outside its BH3 domain50, and DAPK2 phosphorylates raptor on Ser72119, also contributing to mTOR inhibition. The fact that AMPK and DAPK2 share several mutual pathways and substrates supports a functional link between the two kinases, highlighting the significance of the AMPK-DAPK2 pathway.Interestingly, Beclin-1 is not the only shared target of AMPK and DAPK2. An additional shared substrate of AMPK and DAPK2 is the mTOR binding protein raptor. AMPK inhibits mTOR through direct phosphorylation of raptor on Ser722 and Ser792HEK293T cells, A549, and HCT116 cells were grown in Dulbecco’s Modified Eagle’s medium , supplemented with 10% fetal bovine serum , 4 mM glutamine (GibcoBRL), and combined antibiotics (100 µg/ml penicillin and 0.1 mg/ml streptomycin). HEK293 GFP-LC3B and A549 DFCP1-GFP were grown also in the presence of 1 mg/ml G418. Cells were treated with the following reagents: DMSO, ionomycin, phenformin, resveratrol (Sigma-Aldrich), compound C , TRAIL (PeproTech), A-769662 (InvivoGen). All cell lines were purchased from the ATCC and routinely tested for mycoplasma. No cell lines used in this paper were listed in the database of commonly misidentified cell lines maintained by ICLAC.L, Beclin-1, and DAPK2 into the Gaussia luciferase plasmids, a cDNA library was generated from HEK293 cells using RNAeasy MiniElute Cleanup kit (QIAGEN) for mRNA production and SuperScript First-Strand kit (Invitrogen) for RT-PCR. The different genes were amplified with specific primers containing additional restriction sites for BSPEI and XbaI, or NotI and ClaI for the insertion into GLuc(1)-X or X-GLuc(1)/X-GLuc(2) plasmids, respectively. After a standard cloning procedure, the final product was confirmed by direct sequencing of the entire ORF.FLAG-tagged DAPK2 WT and different mutants were expressed from pcDNA3 expression vectors. Control plasmid consisted of empty pcDNA3 plasmid. Point mutations were generated using QuikChange protocol (Stratagene). The sequence of the coding region was confirmed by DNA sequencing. For cloning of Bcl-XHEK293 and HEK293T cells were transfected by standard calcium phosphate technique. A549 were transfected using JetPrime (PolyPlus) for DNA transfection or Lipofectamine2000 (Thermo Fisher Scientific) for siRNA transfection. HCT116 cells were transfected using JetPEI (PolyPlus) for DNA transfection or Dharmafect2 (Dharmacon) for siRNA transfection. All transfections were performed according to the manufacturer’s protocol.DAPK2 expression was transiently knocked down in cells using siGENOME SMARTpool siRNA reagent  against human DAPK2 (NM_014326). For control siRNA, siCONTROL non-targeting siRNA was used (Dharmacon).4 pH 7.5, 5 mM EDTA, 100 mM NaCl, 1% Triton X-100, 1% Na deoxycholate, 0.1% SDS) or with TNE buffer , supplemented with 1 μM PMSF and protease and phosphatase inhibitor cocktails (Sigma). Proteins were separated by SDS–PAGE and transferred to nitrocellulose membranes, which were incubated with antibodies against Flag , LC3B , α-tubulin , vinculin , actin , ERK1/2 , active diphosphorylated (Thr183/Tyr185) ERK1/2  , HA , DAPK2 , Gaussia Luciferase , MLC , phosphoSer19-MLC , AMPK , phospho Thr172-AMPK , phospho-Akt substrate  (C-10001), cleaved caspase 3 , cleaved caspase 9  , PARP-1 , or Beclin-1 . Polyclonal antiphospho-Ser289 DAPK2 antibody was raised in rabbits immunized with the phosphorylated peptide CMVRRE(pS)VVNLEN (Bethyl Laboratories), which is 100% identical in human and mouse DAPK2. Polyclonal antiphospho-Thr119 Beclin-1 antibody was raised in rabbits immunized with the phosphorylated peptide CRLKV(pT)GDLF (Bethyl Laboratories). Detection was done with either HRP-conjugated goat anti-mouse or anti-rabbit secondary antibodies (Jackson ImmunoResearch), followed by enhanced chemiluminescence . Protein densitometric analysis was performed using ImageJ (NIH Imaging Software) on scanned blots. Uncropped images of several important immunoblots are presented in Supplementary Figure Cells were lysed in PLB buffer (10 mM NaPOProtein extracts were incubated with anti-FLAG M2 beads or anti-HA beads (Sigma-Aldrich). After multiple washes, the immunoprecipitated protein was eluted from the beads with an excess of FLAG or HA peptides (Sigma-Aldrich).2, 50 µM ATP, protease and phosphatase inhibitor mix (Sigma), 500 mM EGTA or 0.5 mM Ca2+, 1 µM CaM) for 5 min (MLC) or 15 min (Beclin-1) at 30 °C. Reactions were stopped with sample buffer and resolved by SDS–PAGE.DAPK2 kinase assay: 30 ng FLAG-DAPK2 immunoprecipitated from HEK293T cells was incubated with 2 µg MLC or 750 ng Beclin-1 (ProSpec) in DAPK2 kinase assay buffer  (Sigma) in kinase assay buffer  for 30 min at 30 °C. Reactions were stopped with sample buffer and resolved by SDS–PAGE. Gels were dried and radioactive signal was detected using Amersham Hyperfilm and 32P intensifying screen (Kodak). For cold kinase assay, 1 µg DAPK2 K42A or 1 µg DAPK2 K42A S289A was incubated with 200 ng recombinant AMPK in kinase assay buffer for 30 min at 30 °C and reactions resolved by SDS–PAGE for western blotting.AMPK kinase assay: For radioactive kinase assay, 1 µg DAPK2 K42A or 1 µg DAPK2 K42A S289A purified from AMPK/DAPK2 double kinase assay: 75 ng DAPK2 was incubated with 100 ng recombinant AMPK (α1/β1/γ1) (Sigma) in AMPK kinase assay buffer (for 30 min at 30 °C). Next, 40% of the reaction volume was diluted in DAPK2 kinase assay buffer containing 400 ng MLC or 1 µg Beclin-1, and incubated for 5 min or 10 min, respectively, as described above.Animal experiments were consistent with the National Israeli guidelines for the care and use of laboratory animals, and were approved by the Technion Inspection Committee on the Constitution of the Animal Experimentation. Adult male CD-1 mice  were purchased from Envigo RMS (Israel), and animal care was provided by specialized personnel in the institutional animal care facility. In fasting experiments, mice were deprived of food for 24 or 48 h (water was available ad libitum), and following euthanasia, tibialis anterior muscles were excised and stored at −80 °C.g, the supernatant  was collected.Mouse tibialis anterior muscles were homogenized and lysed in homogenization buffer . After centrifugation at 6000 × Muscles excised from fed or fasted mice were snap-frozen in isopentane, and cross-sections were fixed in 4% PFA. Cross-sectional areas of fibers in 10-μm muscle sections were measured using Imaris (Bitplane), and data collected from at least 500 fibers from four fed or fasted mice were plotted. Images were collected using a Nikon Ni-U upright fluorescence microscope with a Plan Fluor 20 Å ~0.5 NA objective lens with a Hamamatsu C8484-03 cooled CCD camera and MetaMorph software.A total of 2 µg DAPK2 K42A was incubated with or without 200 ng AMPK in kinase assay buffer for 10 min at 30 °C. Reactions were stopped with sample buffer and resolved by SDS–PAGE. The gel was stained with GelCode Blue (Pierce), and gel bands excised and subjected to mass spectrometric analysis at the Smoler Proteomics Center at the Technion . The proteins in the gel were reduced with 10 mM DTT, modified with 40 mM iodoacetamide and trypsinized (modified trypsin (Promega)) at a 1:100 enzyme-to-substrate ratio. The resulting tryptic peptides were resolved by reverse-phase chromatography on 0.1 × 200-mm fused silica capillaries  packed with Everest reversed phase material . The peptides were eluted with linear 120 min gradients of 5–95% of acetonitrile with 0.1% formic acid in water at flow rates of 0.4 µl/min. Mass spectrometry was performed by an ion-trap mass spectrometer  in a positive mode using repetitively full MS scans followed by collision induced dissociation (CID) of the 5 most dominant ions selected from the first MS scan. The mass spectrometric data was clustered and analyzed using the Sequest software and Pep-Miner, searching against the human sequences within the NR-NCBI database.21.Fragments of the Guassia luciferase (referred to as L1 (amino acids 1–93) and L2 (amino acids 94–169)) were fused to the indicated proteins and transfected in pairs into HEK293T cells. Cell lysis and luminescent readings were performed, as described in ref. 2HPO4 0.2 M, 6.4 ml DDW, 50 μl H2O2 and 10 mg ABTS) and color reaction was quantified using ELISA plate reader. To verify equal loading of the different DAPK2 mutants, wells were coated with the DAPK2 used in the assay, and the amount of DAPK2 was quantified as described above.96-well ELISA MaxiSorp plates (Nunc) were coated with 1 µg CaM (Sigma P2277) and incubated at 4 °C overnight. After washing in PBST and blocking in blocking solution , wells were incubated with 80 ng DAPK2 for 15 min at 30 °C, washed again and incubated with anti-DAPK2 antibody (Abcam ab51601) followed by HRP-conjugated goat anti-rabbit secondary antibody (Jackson ImmunoResearch). The wells were further incubated with fresh ABTS substrate solution . For every transfection, ~250 GFP-positive cells were counted from three separate fields and marked as “blebbed” or “not blebbed”.HEK293 cells stably expressing GFP-LC3B were plated onto 18 mm glass coverslips coated with poly-L-Lysine (Sigma-Aldrich) and treated with 5 mM phenformin for 2 h or left untreated as control. A549 cells stably expressing DFCP1-GFP were plated onto 18 mm glass coverslips and treated with 10 μM ionomycin or with DMSO as control. For imaging, cells were fixed with 4% PFA and viewed by fluorescent microscopy (Olympus BX41) with x60 (N.A. 1.25) UPlan-Fl oil immersion objective, and digital images obtained with a DP72 CCD camera using CellSens Standard software (Olympus). For HEK293 GFP-LC3B cells, puncta area/cell area was calculated by analyzing 15–20 different fields, each containing ~100 cells, for each transfection. For A549 DFCP1-GFP cells, puncta area/cell area was calculated by analyzing 20 different fields, each containing ~10 cells, for each transfection. The total area of GFP puncta per total cell area of the population of GFP-expressing cells was quantified by extracting the puncta signals using the Top Hat algorithm of MetaMorph (Molecular Device), and calculating the total area of the dots.hMLC was cloned into the His-bsSumo (K151) expression vector using Transfer-PCR. An Ala residue was incorporated at the N-terminal part of hMLC in order to facilitate cleavage of hMLC from the His-bdSumo tag. Expression was performed in E coli Bl21(DE3) cells. Protein production was induced by addition of 0.2 mM IPTG at mid-log phase and the cells were grown overnight at 15 °C. Cells were lysed using a cell disrupter (Constant Systems) at 4 °C in buffer A . After centrifugation, the clarified supernatant was passed over a HiTrap chelating HP 5 ml column . 1 ml bdSumo protease, bdSENP1, without His tag was added to the column which was left at 4 °C overnight. The cleaved protein was collected by washing the column with 10 ml buffer A. hMLC was further purified by size-exclusion chromatography  equilibrated with 50 mM Tris pH 8, 0.1 M NaCl.DAPK2 K42A and K42A S289A were cloned into the pET28-TevH expression vector using Transfer-PCR. Plasmids were expressed in Escherichia coli BL21(DE3) cells. Proteins were purified using excess peptide.51.Multiple sequence alignment of DAPK2 orthologues was performed using the ClustalOmega algorithmAlignment of DAPK1 and DAPK2 was performed using the Protein BLAST algorithm (blast.ncbi.nlm.nih.gov).52.Molecular graphics and analyses were performed with the UCSF Chimera package11), which was not crystalized with ATP. In the final image, only the human DAPK2 dimer is shown, and the ATP molecule is positioned as in the mouse DAPK2 structure.To properly locate the ATP molecule in the catalytic cleft, the structure of mouse DAPK2 in complex with ATP (PDB entry 2YAA) was superposed on the structure of the human DAPK2 dimer (PDB code 2A2AP < 0.05 were considered significant.Statistical analyses were performed using GraphPad Prism software. Values of The data that support the findings of this study are available from the corresponding author upon reasonable request.Supplementary Information


S phase and mitotic onset are brought about by the action of multiple different cyclin-CDK complexes. However, it has been suggested that changes in the total level of CDK kinase activity, rather than substrate specificity, drive the temporal ordering of S phase and mitosis. Here, we present a phosphoproteomics-based systems analysis of CDK substrates in fission yeast and demonstrate that the phosphorylation of different CDK substrates can be temporally ordered during the cell cycle by a single cyclin-CDK. This is achieved by rising CDK activity and the differential sensitivity of substrates to CDK activity over a wide dynamic range. This is combined with rapid phosphorylation turnover to generate clearly resolved substrate-specific activity thresholds, which in turn ensures the appropriate ordering of downstream cell-cycle events. Comparative analysis with wild-type cells expressing multiple cyclin-CDK complexes reveals how cyclin-substrate specificity works alongside activity thresholds to fine-tune the patterns of substrate phosphorylation.  •Global analysis of CDK substrates and their phosphorylation dynamics in fission yeast•CDK substrate phosphorylation can be temporally ordered by a single cyclin-CDK•Substrate-specific activity thresholds set phosphorylation timing and cell-cycle order•Cyclin-substrate specificity provides a secondary, more minor, level of regulation A systems analysis of CDK substrates in yeast shows that the phosphorylation of different CDK substrates can be temporally ordered during the cell cycle by a single cyclin-CDK via rising CDK activity and the differential sensitivity of substrates to CDK activity. The proper timing and alternation of DNA replication (S phase) and chromosome segregation (mitosis) is essential for the faithful propagation of the eukaryotic genome and the production of viable daughter cells. Progression from G1 into S phase and from G2 into mitosis is controlled by the protein kinase activities of multiple different cyclin-CDK (cyclin dependent kinase) complexes, which are differentially expressed during the cell cycle. The biochemical characterization of budding yeast and metazoa cyclin-CDK complexes has illustrated that different cyclin-CDK complexes can exhibit distinct specificity toward subsets of substrates . This haXenopus egg extracts  can substitute for the four cyclin-CDK complexes acting during the mitotic cell cycle and the six cyclin-CDK complexes acting during the meiotic cell cycle    to overcΔ puc1Δ)  and phos network . To overWe defined in vivo CDK substrates by analyzing the phosphoproteome after inactivating CDK. Cells expressing an ATP analog-sensitive CDK allele were synchronized in mitosis or S phase, and CDK was inactivated by the addition of the ATP analog 1-NmPP1  FiguresA–S1D. PhS. cerevisiae CDK targets , which decreased more than 2-fold after CDK inactivation and whose dephosphorylation was immediate and continuous, fitting an exponential decay (n = 275)  FiguresG. These  targets  . To understand how these patterns in phosphorylation are achieved, we set out to assay the changes in this CDK activity during the cell cycle.Various estimates of CDK activity during the cell cycle have been reported across different eukaryotic systems. These have mostly measured the rate of phosphorylation of a model substrate in vitro . More recdc13-L-cdc2AF(T14AY15F)) (50) of 1-NmPP1 values are 3.7 μM, 1.7 μM, and 0.2 μM for early, mid, and late substrates, respectively. Consistent with this, later substrates are re-phosphorylated slower than earlier substrates after transient CDK inactivation in mitosis  concentrations and analyzing the phosphoproteome E. This a4AY15F)) . Figures mitosis D. These There is also a correlation among late substrates between their phosphorylation during G2/M and their sensitivity to CDK activity E and S3J50  between sites on the same protein are significantly smaller than those between sites on different proteins, demonstrating that sites on the same protein behave more similarly to one another. We also note that early substrates tend to be less abundant than late CDK substrates . Because ΔCCP cells typically have an extended G1 that is dependent on the CDK inhibitor Rum1  somewhat sharpens the rise in early substrate phosphorylation across G1/S  phosphosites that decreased to a similar extent. This means that if G1/S cyclin-unique substrates do exist in S. pombe, then they can only represent a very small fraction of CDK substrate sites .Here, we report a phosphoproteomics-based systems analysis of CDK substrate phosphorylation and demonstrate the importance of substrate-specific CDK activity thresholds for the temporal ordering of S phase and mitosis, using the model eukaryote We have shown that clearly distinguished early and late patterns of substrate phosphorylation are observed when the cell cycle is driven by a single cyclin-CDK . The in can happen as opposed to what does happen  are robustly phosphorylated early in the cycle in cells run by a single cyclin-CDK. Second, when G1/S cyclin-CDKs are present, these early substrates are not subject to any obvious cyclin-substrate specificity; instead, G1/S cyclin-CDKs contribute to early substrate phosphorylation by providing additional generic CDK activity. Finally, when G1/S cyclins are present, we only observed substrates whose phosphorylation pattern was advanced from mid to early, never late to early. Therefore, where G1/S cyclin-substrate specificity does exist, it is always combined with an increased substrate sensitivity to generic CDK activity. It will be of interest to examine how and why some substrates are subject to G1/S cyclin specificity, while others are appropriately phosphorylated at G1/S by an elevated sensitivity to generic CDK activity alone.Previous studies that have implicated activity thresholds have relied on certain genetic perturbations to test what s happen . Our phos happen . This shThese conclusions contrast with the generally accepted view that the major differences in the timing of CDK substrate phosphorylation are brought about by the biochemical specificity of different cyclin-CDK complexes toward sub-pools of certain substrates . InsteadS. pombe, so it will be important to examine the role played by activity thresholds in other model eukaryotes. Clearly, the relative contribution made by cyclin specificity and quantitative changes in generic CDK activity may differ between systems, but the fact that there are important examples of cyclin-substrate specificity in other organisms does not preclude activity thresholds playing a substantial or even predominant role in those systems. Indeed, there are numerous reports of plasticity in the metazoan cell-cycle network, which genetically or biochemically demonstrate how certain cyclin-CDKs can fulfill the functions of other cyclin-CDK complexes  in late G2 and on the spindle during mitosis  in Sld3 and Mdb1 are required for their enhanced sensitivity to CDK activity E. This sIn vitro studies of substrate competition have demonstrated that the presence of higher-affinity CDK targets can inhibit CDK-mediated Wee1 phosphorylation and also enhance the non-linear response of Wee1 phosphorylation . One posAn important component to any such kinase-phosphatase signaling network is the phosphorylation turnover. Our measurements do not provide evidence for differences in phosphorylation turnover between early and late substrates B. They dmatthew.swaffer@crick.ac.uk).Further information and requests for reagents may be directed to and will be fulfilled by the Lead Contact, Matthew P. Swaffer (S. pombe media and standard methods are as previously described (cdc13-L-cdc2(as) Δ2 Δ13 ΔCCP, cdc13-L-cdc2AF(as) Δ2 Δ13 ΔCCP (ΔCCP (lys3-37 arg1-230 car2Δ (sld3-5flag (rad3Δ (cdc2(as)M17 (orc2-v5), gene deletion (rum1Δ) and gene deletion marker switching was performed as previously described -v5, sld3-v5, sld3(rxl:aaa)-v5, mdb1-v5 or mdb1(rxl:aaa)-v5 (IDT and Life Technologies) were sub-cloned downstream of the moderate strength nmt41 promoter in a Rip41x vector. NB the full RxL motif sequence is R/K-X-L-X-F/Y/L/I/V/M/P and RxL:AAA constitutes the replacement of R/K-X-L with AAA. To generate Rip3x-drc1-v5, Rip3x-sld3-v5 and Rip3x-sld3(rxl:aaa)-v5 constructs the nmt41 promoter in the above Rip41x constructs was converted to the high strength nmt1 promoter using Q5 site directed mutagenesis (NEB). The above Rip3x and Rip41x constructs were PacI linearized and transformed to generate MS306-MS319. Stable leucine prototrophy was selected for and expression was confirmed by western blotting.escribed . Full geΔ13 ΔCCP , ΔCCP M17 . Additioescribed . DerivatAll experiments were performed in exponential growth. All experiments for proteomics were performed in SILAC adjusted media (EMM (6 mM ammonium chloride) + 0.25 mg/ml leucine, 0.15 mg/ml uridine, 0.04 mg/ml arginine and 0.03 mg/ml lysine) , unless Cell-cycle progression was monitored by DNA content and cell & nuclear division. DNA content was determined by flow cytometry (FACS): ethanol  fixed cells were washed and resuspended in 50 mM sodium citrate before incubation with RNase A (0.1 mg/ml) (SIGMA) . DNA was then stained with propidium iodide (2 μg/ml) (SIGMA) before sample sonication and DNA content per event was acquired for 10,000 events on a BD LSR-Fortessa. DNA content is displayed on a log scale after gating for single, whole cells in FlowJo X (Treestar Inc). Nuclear division and cell division were scored in heat fixed samples by monitoring DNA  and septum formation , respectively. Cell size at division was measured from live septated  cells. Samples were imaged on a Zeiss Axioskop microscope, 63 × /1.4 NA objective and cell photos were taken with a QICAM Fast Digital Camera.Protein samples were taken as follows. Cell cultures were quenched by adding 100% (w/v) ice cold trichloroacetic acid (TCA) to a final concentration of 10%. Cells were kept on ice for > 20 min, pelleted and washed in −20°C acetone. Cell pellets were then washed and resuspended (100-200 μl) in lysis buffer (8 M urea + 50 mM ammonium bicarbonate + cOmplete Mini EDTA-free protease inhibitor cocktail (Roche) + PhosSTOP phosphatase inhibitor cocktail (Roche)). 1.2 ml acid washed glass beads  were added to cell suspensions, which were then beaten to break cells (FastPrep120). Cell debris was pelleted  and the supernatant was recovered as a protein sample (stored at −80°C). Protein samples for Lambda phosphatase (NEB) treatment were extracted as above, with the omission of PhosSTOP.Phosphorylation dependent mobility shifts for Orc2, Sld3, Bir1 and Mdb1 were resolved using Phos-tag (Alpha Laboratories) supplemented SDS-PAGE under neutral pH conditions . Protein18 SepPak Lite (130 mg bed volume) under vacuum and dried. To ensure complete digestion, peptides were further digested using Lys-C in 10% acetonitrile, 50 mM ammonium bicarbonate , followed by trypsin digestion . Digested peptides were then desalted again and dried.Heavy labeled or light labeled protein samples were mixed with a light labeled or heavy labeled reference protein sample respectively. Protein samples were mixed in a 1:1 protein amount ratio (protein concentration determined by Bradford assay (Bio-Rad)). 1mg of protein sample (after mixing) was reduced with 5mM dithiothreitol (DTT) , alkylated with 10 mM iodoacetamide  and quenched with 7.5 M DTT. Samples were then diluted with 50 mM ammonium bicarbonate to reduce the urea concentration to < 2 M, prior to trypsin digestion . Peptides were acidified to 0.4% trifluoroacetic acid (TFA) and centrifuged . Peptides were then desalted using a C2) was carried out as follows. Dried peptide mixtures were re-suspended in 1 M glycolic acid + 80% acetonitrile + 5% trifluoroacetic acid, sonicated (10 min) and added to titanium dioxide beads (5:1 (w/w) beads:protein). The beads were washed using 80% acetonitrile + 1% trifluoroacetic acid followed by 10% acetonitrile + 0.2% trifluoroacetic acid, and dried under vacuum centrifugation. Flow-through fractions were retained for analysis of non-phosphorylated peptides. Phosphopeptides were eluted from the beads by adding 1% ammonium hydroxide followed by 5% ammonium hydroxide, and dried by vacuum centrifugation. Dried phosphopeptides were re-suspended in 100 μl of 1% trifluoroacetic acid and sonicated (15 min). A C18 membrane was packed into a 200 μl pipette tip and washed using methanol and equilibrated with 1% trifluoroacetic acid. The peptides were loaded onto the Stage Tip and washed with 1% trifluoroacetic acid followed by elution with 80% acetonitrile + 5% trifluoroacetic acid. The eluted peptides were again dried under vacuum centrifugation. For analysis of non-phosphorylated peptides, the stored flow-through fractions were dried, desalted using a C18 SepPak Lite (130 mg bed volume) under vacuum and dried. Non-phosphorylated peptides were separated into 12 fractions using strong cation exchange (SCX) liquid chromatography  and dried to 250 μl.Phosphopeptide enrichment using titanium dioxide (TiO18 column) with one activation method per run: Collision Induced Dissociation (CID), Multi-Stage Activation (MSA) and Higher energy collision Dissociation (HCD). Non-phosphopeptide mixtures were diluted 1:10 (v/v) in 0.1% trifluoroacetic acid and injected three times (10 μl per injection). Each run consisted of a 3 hr gradient elution (75 μm × 50 cm C18 column) with CID used as the activation method.An LTQ-Orbitrap Velos was used for data acquisition of phosphopeptides and an LTQ-Orbitrap Velos Pro was used for data acquisition of non-phosphorylated peptides. Both instruments were coupled to UltiMate 3000 HPLC systems for on-line liquid chromatographic separation. Phosphopeptide mixtures were re-suspended in 35 μl 0.1% trifluoroacetic acid and injected three times (10 μl per injection). Each run consisted of a 3 hr gradient elution . A decoy database containing reverse sequences was used to estimate false discovery rates and set the false discovery rate at 1%. Default MaxQuant parameters were used with the following adjustments: Phospho(STY) was added as a variable modification (for the phospho-samples only), Lys6 and Arg6 were the heavy labels, ‘Filter labeled amino acids’ was deselected, re-quantify was selected with the instruction to keep low-scoring versions of identified peptides within parameter groups and match between runs was selected. Experiment and sample codes used to name MaxQuant output files are listed in MaxQuant (version 1.3.0.5) was used for all data processing. The data was searched against a UniProt extracted MaxQuant output files were imported into Perseus (version 1.4.0.2) and the normalized heavy-to-light (H:L) ratios were used for all subsequent analysis. For phosphopeptides, only phosphosites with > 0 valid values quantified and a localization probability > 0.9 were used for subsequent data analysis, after removal of reverse and contaminant peptides. Data imputation and smoothing was performed in R. All linear regressions, non-linear curve fitting , interpolation, AUC (Area Under Curve) calculations, Spline calculation and Mann-Whitney U tests were performed in Prism 6 on data exported from Perseus.i) Given M00 is a comparison between biological replicates  the CCC5978 dataset was filtered to only include phosphosites for which 2 > H:L (M00) > 0. 5  = 1)). CCC5977 was filtered to only include phosphosites identified in S00 for which H:L(S00) > 0.25. ii) Phosphosites that had ratios measured in less than four out of seven time points were excluded. iii) Sites that had a H:L ratio at 1, 3, 6, 9 and/or 12 min after CDK inactivation less than half the value of the initial time point (0 min) were kept. iv) A non-linear one-phase exponential decay was fitted to the data using the least-squares (ordinary) fitting method and was used to calculate phosphorylation half-life and dephosphorylation rate values (Prism 6) (related to i-iv), and if a site was now defined as a substrate it was included as a substrate with the respective anomalous value removed before calculation of the phosphorylation half-life and dephosphorylation rate. Where the removal of multiple values independently permitted a site to pass the four above criteria, the one that gave the highest one-phase exponential decay R squared value was used. Phosphosites defined as CDK substrates and the corresponding half-life and dephosphorylation rate values are listed in The relative phosphorylation of phosphosites after CDK inactivation in S phase and mitosis was used to define CDK substrates. The CCC5977 (S phase) and CCC5978 (mitosis) datasets were filtered for phosphorylation events at S/T-P . The following four criteria were the used to define CDK substrate sites G. i) Givi-iv in both experiments. NB anomaly screening was not performed for this analysis.For the comparison of half-lives between biological repeats B, half-lS. pombe protein ortholog list  on datasets with missing values imputed (sees above). Related to CDK substrate phosphorylation rates were quantified as follows. Substrate sites that were dephosphorylated after transient CDK inactivation ) were analyzed to calculate phosphorylation rates and the respective median relative phosphorylation values for early, mid and late substrates. A linear regression line was fitted (Prism 6) to values during the period of approximately linear increase in relative phosphorylation for individual substrate sites, and the median relative phosphorylation of early, mid and late substrate sites. Relative rate measurements were derived from the slope of the linear regression line. Related to 50 and Hill slope values. IC50 and Hill slope values for CDK substrate sites are listed in The relative phosphorylation of CDK substrate sites across a range of 1-NmPP1 concentrations was fitted to a non-linear sigmoidal curve (four-parameter logistic function) using the least-squares (ordinary) fitting method (Prism 6). The curve was constrained such that the Hill slope < 0 and Bottom > 0. Outlier detection was used (Q = 10%). Phosphosites with fitted curves  were used to derive ICThe total relative phosphorylation ) was calculated (Prism 6) as a proxy for the timing and extent of a site’s phosphorylation during progression from early G2 through to mitosis (50-100 min after release). AUC values were calculated from data after imputation to replace missing values and data smoothing. The peak in binucleation during this mitosis is < 50% synchronous B meaningwt / ΔCCP or ΔCCP rum1Δ / ΔCCP was calculated to give to give a semiquantitative estimate of G1/S cyclin specificity in vivo (A.U.). Substrates subject to G1/S cyclin specificity will give an in vivo specificity score > 1 in wt / ΔCCP and ∼1 in ΔCCP rum1Δ / ΔCCP. Related to The extent of cyclin-substrate specificity was assessed by calculating an in vivo G1/S cyclin specificity score for early and mid substrates as follows. Sigmoidal curves fitted to the median relative phosphorylation values of early and mid substrates across a range of 1-NmPP1 concentrations E were ushttp://www.ebi.ac.uk/pride/archive/) under the dataset identifier PRIDE: PXD003598. Raw data, all MaxQuant output files and MaxQuant output Phospho (STY)Sites.txt and ProteinGroups.txt files after imputation and smoothing have been deposited. File names and the corresponding experimental samples are listed in Mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository (M.P.S., A.P.S., and P.N. conceived the experiments. M.P.S. designed and performed the experiments. A.W.J. and H.R.F. performed mass spectrometry and processed raw data. M.P.S. analyzed processed data. M.P.S. and P.N. wrote the manuscript.


Dynamic changes in protein phosphorylation govern the transitions between different phases of the cell division cycle. A “tug of war” between highly conserved protein kinases and the family of phosphoprotein phosphatases (PPP) establishes the phosphorylation state of proteins, which controls their function. More than three-quarters of all proteins are phosphorylated at one or more sites in human cells, with the highest occupancy of phosphorylation sites seen in mitosis. Spatial and temporal regulation of opposing kinase and PPP activities is crucial for accurate execution of the mitotic program. The role of mitotic kinases has been the focus of many studies, while the contribution of PPPs was for a long time underappreciated and is just emerging. Misconceptions regarding the specificity and activity of protein phosphatases led to the belief that protein kinases are the primary determinants of mitotic regulation, leaving PPPs out of the limelight. Recent studies have shown that protein phosphatases are specific and selective enzymes, and that their activity is tightly regulated. In this review, we discuss the emerging roles of PPPs in mitosis and their regulation of and by mitotic kinases, as well as mechanisms that determine PPP substrate recognition and specificity. Transitions between distinct phases of the cell cycle are governed by post-translational modifications of proteins. Protein phosphorylation is the most prevalent post-translational modification, with more than three-quarters of the human proteome being phosphorylated  activity, chromosome condensation, and microtubule aster formation , phosphotyrosine phosphatases (PTP), and aspartate-based protein phosphatases , Mg2+, Fe2+, and Zn2+ ions) in the catalytic center. The metal ions in the catalytic center of PPPs are crucial for the activation of a water molecule which triggers a nucleophilic attack on the phosphorous atom of the substrate phosphate group to hydrolyze the phosphate ester bond, thereby dephosphorylating the substrate , PP4, PP5, PP6, and PP7 Table . These a‴) , but differ in tissue expression and have specific functions by associating with isoform-specific partners  Table . Theoret‴) Table . PP4 andEntry into mitosis is often characterized as a dramatic increase in “bulk” phosphorylation that needs to be reversed by “bulk” dephosphorylation to allow cells to exit mitosis. These changes in protein phosphorylation are in general thought to be accompanied by a rise in kinase activity, most importantly Cdk1 activity, and a reduction in PP1 and PP2A phosphatase activities early in mitosis, followed by a reversal of the respective activities as cells start to exit mitosis. However, regulation of phosphorylation signaling in all phases of mitosis is highly dynamic requiring coordination of opposing kinase and PPP activities in a specific temporal and spatial manner to ensure orderly and accurate progression through mitosis to generate identical daughter cells , PP1 reverses AURKB activation loop phosphorylation (Thr-232 in human cells) at kinetochores  surrounded by linear sequence motifs containing either basic amino acids or acidic amino acids or proline  were more rapidly dephosphorylated than pSP sites and that this is due to the action of the phosphatase PP2A-B55  are 100% identical Shi, . How canBesides directly influencing the biochemical characteristics of the phosphorylation sites, short linear motifs (SLiMs) also contribute to the assembly of PPP holoenzymes and the recognition of substrates. SLiMs are often located in intrinsically disordered regions of proteins and the sequence composition of the SLiM regulates protein-protein interaction and binding affinity. SLiM-mediated interactions promote PPP holoenzyme assembly, substrate specificity, phosphatase activity, and subcellular targeting. The use of SLiMs as a primary mechanism for catalytic and regulatory subunit assembly was first discovered for PP1 and PP2B   through which they associate with the catalytic subunit  governing both classes of enzymes, it is becoming clear that protein kinases and phosphatases not only counteract each other on their shared substrates but also counteract each other directly as well as indirectly through elaborate feedforward and feedback loops. Identification of regulatory mechanisms governing the activity of distinct PPP holoenzymes including phospho-amino acid (Ser vs. Thr) and phosphorylation site motif (acidophilic vs. basophilic) preferences have greatly increased our knowledge of dephosphorylation signaling and demonstrate the importance of investigating PPP holoenzyme specific substrate preferences The identification of SLiM mediated subunit-subunit and subunit-substrate interaction mechanisms has greatly enhanced our ability to predict PPP regulatory subunits and substrates and build PPP-based networks. SLiMs provide an entry point for specifically targeting PPPs to discover their biological functions and substrates as well as roles in diseases. Phosphorylation signaling is frequently disrupted or deregulated in many types of cancer. Until now, the focus for therapeutic strategies has been on protein kinases. However, the inevitable emergence of kinase inhibitor resistance requires new therapeutic approaches and targeting regulatory PPP subunits and their interactions with catalytic subunits may prove to be a viable strategy. PPP inhibition activates the spindle assembly checkpoint and blocks mitotic exit, ultimately leading to mitotic catastrophe and cell death, a strategy often applied in cancer therapy. While global inhibition of PPP activity impacts all cells, targeting specific PPP holoenzymes via the SLiM motif could be tailored to signaling pathways frequently deregulated in cancer. For instance, selective inhibition of PP1 in mitosis has been shown to cause impaired tumor growth (Winkler et al., Another important area of investigation in PPP biology is the identification of substrates of specific PPP holoenzymes in order to connect kinases and PPPs on their shared substrates. Quantitative mass spectrometry-based proteomic and phosphoprotoemic approaches combined with small molecule kinase inhibitors, genetically-engineered ATP-analog sensitive kinases, substrate trapping mutants, proximity-labeling and crosslinking approaches have identified a plethora of kinases substrates. Using similar approaches when applicable and available, as well as inducible degron strategy and SLiM targeting peptides to identify the phosphatase substrateswill fill the gaps in our understanding of phosphorylation signaling networks. In addition, identification of upstream signaling inputs regulating PPP holoenzyme assembly, activity and substrate targeting will be important to dissect the complex regulatory behaviors.IN and AK wrote the manuscript and made a direct, intellectual contribution to the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.