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I was reading a paper by Tang & Othmer about oscillations and waves in Dictyostelium discoideum. Under certain condition like starvation period in the life cycle of a Dictyostelium discoideum community, they evolve itself into a multicellular organism in a process of cellular aggregation. I have read about Pacemaker cells among these individuals which transmits periodic cAMP and attracts other cells towards them.
So I was wondering: When and How do these Pacemaker cells develop from a "normal" cell into a pacemaker cell excreting cAMP?
References, links or answers would be very helpful for me.
Desynchronization of cells on the developmental path triggers the formation of spiral waves of cAMP during Dictyostelium𠂚ggregation
Whereas it is relatively easy to account for the formation of concentric (target) waves of cAMP in the course of Dictyostelium discoideum aggregation after starvation, the origin of spiral waves remains obscure. We investigate a physiologically plausible mechanism for the spontaneous formation of spiral waves of cAMP in D. discoideum. The scenario relies on the developmental path associated with the continuous changes in the activity of enzymes such as adenylate cyclase and phosphodiesterase observed during the hours that follow starvation. These changes bring the cells successively from a nonexcitable state to an excitable state in which they relay suprathreshold cAMP pulses, and then to autonomous oscillations of cAMP, before the system returns to an excitable state. By analyzing a model for cAMP signaling based on receptor desensitization, we show that the desynchronization of cells on this developmental path triggers the formation of fully developed spirals of cAMP. Developmental paths that do not correspond to the sequence of dynamic transitions no relay-relay-oscillations-relay are less able or fail to give rise to the formation of spirals.
The aggregation of Dictyostelium discoideum amoebae after starvation provides one of the best examples of spatiotemporal pattern formation at the supracellular level. This transition from a unicellular to a multicellular stage of the life cycle occurs by a chemotactic response to cyclic AMP (cAMP) signals emitted by aggregation centers in a periodic manner (1𠄳). Amoebae are capable of relaying the signals emitted periodically by a center located in their vicinity. This excitable response to periodic signals explains the wavelike nature of aggregation over territories whose dimensions can reach up to 1 cm: within each aggregation territory, the amoebae move toward a center in concentric or spiral waves with a periodicity of the order of 5 to 10 min (4𠄶). Waves of cellular movement correlate with waves of cAMP (7) the latter present a striking similarity to waves observed in oscillatory chemical systems such as the Belousov–Zhabotinsky reaction (8).
As shown by computer simulations using a model for cAMP relay and oscillations based on receptor desensitization proposed by Martiel and Goldbeter (9, 10), concentric waves can readily be explained by assuming the existence of a pacemaker generating periodic pulses of cAMP in the midst of a field of excitable cells. It is much more difficult to explain the origin of spontaneously occurring spiral waves of cAMP. A common artifice to obtain spirals, also used for Dictyostelium (11), is to break concentric or planar waves as the medium is excitable, spirals develop at the extremities of the broken wave. More recently, Pálsson and Cox (15) have used the above-mentioned model (9) to show that the random generation of cAMP pulses after the passage of a wave can give rise to the formation of spirals. Levine et al. (16) also considered the random generation of cAMP pulses in a hybrid model including cAMP production and cell movement and showed that the development of spirals was favored by the feedback exerted by cAMP signals on the excitability of the system. Incorporation of the variation of cell density due to chemotaxis was also shown (17) to favor, in the presence of a pacemaker, the spontaneous formation of spiral waves.
Here we propose a physiologically plausible scenario, only based on cellular properties, for the onset of spiral waves of cAMP at the early stages of D. discoideum aggregation. We take into account the ontogenesis of the cAMP signaling system by allowing it to evolve on the developmental path (18, 19) that brings this system successively from a nonexcitable state to a state in which it displays the relay property, and from such an excitable state into the domain of sustained oscillations of cAMP, before the system becomes excitable again. The transitions between the different modes of dynamic behavior are brought about by continuous changes in biochemical parameters such as the activity of adenylate cyclase and phosphodiesterase in the hours after starvation. Our results indicate that spiral waves of cAMP naturally originate from the desynchronization of cells on the developmental path.
Model for cAMP Signaling Based on Receptor Desensitization
In the model for cAMP oscillations in Dictyostelium based on the reversible desensitization of the cAMP receptor (9, 10), extracellular cAMP binds to the receptor, which exists in two states (20), one of which is active (R) and the other desensitized (D). Only the complex formed by cAMP with the receptor in the R state is capable of activating adenylate cyclase, the enzyme synthesizing cAMP. A positive feedback loop arises from the transport of intracellular cAMP into the extracellular medium where it binds to the cAMP receptor and is hydrolyzed by phosphodiesterase. This model accounts for the oscillatory synthesis of the cAMP signal, with a periodicity of 5 to 10 min (21), and for the accompanying, periodic alternation of the receptor between the phosphorylated (D) and dephosphorylated (R) states (22). The model predicts that the interval between two cAMP peaks𠅊nd, consequently, the period of the oscillations—is primarily set by the time required for resensitization of the cAMP receptor.
The model for cAMP signaling is governed by the following system of three differential equations giving the time evolution of the total fraction of active cAMP receptor (ρT) and the normalized concentrations of intracellular (β) and extracellular (γ) cAMP (9):
As considered by Tyson and Murray (11), we have incorporated into Eq. 1c the diffusion of cAMP into the extracellular medium. In this study, we disregard the chemotactic movement of cells, to better focus on the biochemical and developmental aspects of the mechanism of spiral formation.
Developmental Path for the cAMP Signaling System
Besides accounting for relay of suprathreshold cAMP pulses and for autonomous oscillations of cAMP, the model also provides an explanation for the sequence of developmental transitions no relay-relay-oscillations-relay observed during the hours that follow starvation (23). Although the actual developmental path takes place in a parameter space of higher dimensions, the theoretical analysis shows that the continuous increase in the activities of adenylate cyclase and phosphodiesterase after starvation (24) suffices to account for these developmental transitions (18, 19). In this view, aggregation centers would be those cells that would be the first to reach the domain of sustained, autonomous oscillations of cAMP as a result of a progressive rise in the activities of adenylate cyclase and phosphodiesterase. Such a phenomenon provides a prototype for the ontogenesis of biological rhythms, by showing how continuous changes in biochemical parameters𠅊ssociated here with the synthesis of enzymes or receptorsn lead to the onset of autonomous oscillations once a critical value of a control parameter is exceeded (10).
We have established the diagram showing the different modes of dynamic behavior of the cAMP signaling system as a function of the two main parameters of the model governed by Eqs. 1a, namely the maximum activity (σ) of adenylate cyclase and the rate constant of extracellular phosphodiesterase (ke). Domains S, E, and O in Fig. Fig.1 1 correspond, respectively, to the regions in which the cAMP signaling system reaches a nonexcitable stable steady state, an excitable steady state, or autonomous oscillations of cAMP. Excitability is determined here as the capability of the system to propagate without attenuation a wave of cAMP initiated by a suprathreshold cAMP pulse applied at the center of the spatially distributed system.
Stability diagram showing the different modes of dynamic behavior of the cAMP signaling system in the adenylate cyclase (σ)-phosphodiesterase (ke) parameter space. The different regions are those of a stable, nonexcitable steady state (S), excitability (E) (defined here as the capability of sustaining without attenuation the propagation of a wave of cAMP upon a suprathreshold elevation of extracellular cAMP), sustained autonomous oscillations of cAMP (O), and bistability (B), which denotes the coexistence of two stable steady states (the latter behavior, for which there exists no experimental support so far, is not considered here). The arrows refer to possible developmental paths followed by the signaling system in this parameter space as a result of variation in enzyme activities during the hours that follow starvation. As shown in the text, only path 3 gives rise to the formation of stable, fully developed spirals of cAMP. The diagram has been established by linear stability analysis and numerical integration of Eqs. 1a for the parameter values listed in the last column of table 2 in ref. 9, except the values of k1, k-1, k2, k-2, which have been multiplied by a factor of 2.5 to obtain waves with a period of the order of 5 min in Fig. Fig.6. 6 . For path 2, σ = 0.6 min 𢄡 .
Of the three distinct developmental paths considered in Fig. Fig.1, 1 , only path 3 accounts for the observed sequence of transitions no relay-relay-oscillations-relay. This path corresponds to the progressive increase in the activity of the two enzymes observed during the hours that follow starvation (24). We shall show that the nature of the variation in σ and ke markedly affects the formation of cAMP waves. Developmental path 3 will be compared with paths 1 and 2 for which ke or σ remain constant, respectively as is clear from Fig. Fig.1, 1 , the latter two paths correspond to different sequences of dynamical transitions.
Onset of Spiral Waves of cAMP
We now determine the spatiotemporal evolution of cAMP in a two-dimensional medium represented by a spatial grid of typically 100 × 100 points corresponding to a 1-cm 2 area covered by a layer of cells the results remain qualitatively unchanged when considering a finer spatial grid. The elements of the grid (100 μm × 100 μm) correspond to groups of about 10 cells this represents a density of the order of 10 5 cells/cm 2 , which is above the critical cell density of 2.5 × 10 4 cells/cm 2 found for relay (27). Thus, the present simulations are based on the physiologically plausible assumption (see Discussion) that groups of about 10 cells in each “patch” of the grid behave synchronously with respect to their development.
In contrast to others (15, 16) we do not subject the system to any kind of random or periodic pulsing of cAMP. Searching for physiologically plausible conditions giving rise to the spontaneous formation of spiral waves, we first attempted to incorporate a random spatial distribution of parameters such as σ or/and ke but always failed to generate stable spirals in such conditions. We then included a homogeneous temporal variation in these parameters corresponding to the synchronous evolution of all cells along the developmental paths 1, 2, or 3 considered in Fig. Fig.1. 1 . Again stable spirals were never generated in such a way.
Key to the formation of stable spirals is the desynchronization of cells on the developmental path. Up to 10 5 cells can aggregate around a center it is inevitable that these cells at any moment do not possess exactly the same amounts of enzymes and do not start their development after starvation in the same initial conditions, if only because they are caught by starvation at different stages of the cell cycle (29). Such a heterogeneity due to cell cycle phase distribution was previously invoked to account for the asynchrony in development of the relaying competence (27) and onset of cAMP oscillations (30) in Dictyostelium cells. To characterize this biochemical heterogeneity, we shall consider that at the beginning of starvation cells are distributed along the developmental path. As each point of the path corresponds to a particular time, this is equivalent to considering a distribution of (positive) starting times (ts) for cells evolving along the same developmental path. This will ensure that some cells will be more advanced than others on this path: the larger ts, the more advanced the cells in their development.
The probability of the starting time will be taken as a decreasing exponential:
Parameter Δ measures the desynchronization of cells on the developmental path. The larger Δ, the more heterogeneous the distribution of cells on this path conversely, when Δ = 0, all cells evolve synchronously. Integration of Eq. 3 shows that 90% (99%) of cells will have a value of ts between 0 and 2.3 (4.6) Δ. The exponential distribution was retained to ensure that a small percentage of cells is more advanced than the others on the path, so that a minute fraction will reach first the oscillatory domain. Similar results are obtained when using distributions other than exponential, e.g., uniform over a prescribed time interval.
Shown in Fig. Fig.2 2 are the distributions of starting times along the developmental path 1 (see Fig. Fig.1) 1 ) for Δ = 50 min (gray histogram) and 100 min (blank histogram), respectively. The solid curve in Fig. Fig.2 2 shows the variation of parameter σ as a function of time (ke remains constant along this path). As σ increases, the cAMP signaling system crosses successively the domains (separated by the dotted vertical lines) of nonexcitable steady states (S), excitable states (E), and autonomous oscillatory behavior (O).
Developmental path 1 (see Fig. Fig.1) 1 ) and distribution of starting times on this path. The solid line gives the time evolution of parameter σ (in min 𢄡 ) according to the equation σ(t) = 0.3 + 0.25 tanh [(t − 250)/90] where time t is expressed in min. A similar sigmoidal evolution can be obtained by using a logistic equation for σ(t). The increase in σ brings the signaling system successively through the regions of nonexcitability (S), excitability (E), and autonomous oscillations (O) defined in Fig. Fig.1. 1 . The histograms show the distribution of starting times grouped in 20-min intervals, generated according to Eq. 3 for Δ = 50 min (gray bars) or 100 min (empty bars). The starting time for a particular cell is given by the abscissa of the point on the curve σ(t) at which this cell begins its progression on this curve.
To study the spatiotemporal evolution of the signaling system we consider a spatially random distribution of the values of parameter σ at which this parameter starts to increase on the developmental path this heterogeneous spatial distribution of σ follows from the probability distribution of the starting times given by Eq. 3. In Fig. Fig.3, 3 , we show the spatiotemporal evolution obtained for Δ = 50 min (A) and 100 min (B). In each case, the upper row shows the concentration of extracellular cAMP (γ) at the different times indicated in minutes in the upper left corner of the panels the lower row gives the distribution of cells on the developmental path at the corresponding times (each circle represents 5% of the total number of cells considered). For Δ = 50 min, almost all cells are initially in the nonexcitable state (see also Fig. Fig.2) 2 ) concentric (target) waves of cAMP are seen to develop after about 4 hr. No spiral is observed in these conditions.
Developmental paths and wavelike patterns of cAMP. Shown is the two-dimensional spatial distribution of cAMP (Upper) at indicated times (in min) resulting from the progression of cells along the developmental path 1 (Lower) shown in Fig. Fig.2, 2 , for a desynchronization factor Δ = 50 min (A) and 100 min (B). For the cell distribution along the developmental path, each circle represents a 5% fraction of the total amount of cells considered. Parameter values are as in Fig. Fig.1 1 the diffusion coefficient of cAMP, Dγ, is taken equal to 1.5 × 10 𢄤 cm 2 /min (28). The area of 1 cm 2 is represented as a grid of 100 × 100 points. The spatiotemporal evolution of the system is obtained by integrating Eqs. 1a by means of an explicit Euler method with finite differences for the spatial term in Eq. 1c the time step used was 10 𢄢 min, and the precision of the integration was checked by halving this step. For each point of the grid a starting time on the developmental path is chosen according to the probability distribution shown in Fig. Fig.2 2 the resulting desynchronization of cells on the developmental path creates a spatial heterogeneity in the parameter(s) that vary on this path.
In contrast, for Δ = 100 min, the desynchronization of cells is stronger so that a significant amount of cells rapidly reaches the oscillatory domain and initiates concentric patterns while at that time the other cells are still either in the excitable or nonexcitable state (see Fig. Fig.3 3 B at 180 min). Because of the heterogeneity in the oscillation frequency between emerging pacemakers and in the refractory period among regions of excitable (and nonexcitable) cells, concentric wavefronts are distorted and sometimes break. The loose ends of the broken fronts curl around to form spiral waves (see Fig. Fig.3 3 B at 300 min). These spirals, however, fail to fully develop so as to occupy the whole field, in contrast to those to be described below for path 3. Moreover, bulk oscillations occur in the regions not occupied by spirals, the reason being that for path 1 all cells end in the oscillatory domain.
To determine whether the passage through a domain of excitability before the entry into the oscillatory domain influences the type of wavelike pattern observed, we tested the developmental path 2 of Fig. Fig.1, 1 , in which ke (but not σ) increases. The results (not shown) indicate that only bulk oscillations without any waves occur in these conditions.
We now turn to path 3 of Fig. Fig.1, 1 , which combines an increase in both σ and ke. This path brings the system across regions S, E, O, E successively. Shown in Fig. Fig.4 4 is the time evolution of the two parameters. The distribution of starting times obtained here for Δ = 25 min (spirals also are obtained for larger values of the desynchronization factor Δ see Discussion) is given in Fig. Fig.5. 5 . The temporal sequence of spatial cAMP patterns associated with this evolution of the two biochemical parameters is shown in Fig. Fig.6 6 (Upper), together with the distribution of cells along the developmental path at the times considered (Lower). After some 150 min, concentric waves form, but they are disturbed (see Fig. Fig.6 6 at 180 min) and later break (see Fig. Fig.6 6 at 240 min), again as a result of the heterogeneity both in pacemaker frequency and refractory period. In contrast with the case of path 1 illustrated in Fig. Fig.3, 3 , spirals take over the whole field (see Fig. Fig.6 6 at 360 min) as the majority of cells return into an excitable state. The parameters having reached their asymptotic values, which now correspond to a situation in which all cells are excitable, these stable spirals are maintained, with a period close to 5 min.
Developmental path 3 (see Fig. Fig.1). 1 ). The solid lines give the time evolution of parameter σ (in min 𢄡 ) according to the equation σ(t) = 0.3 + 0.25 tanh [(t − 200)/50] and of parameter ke (in min 𢄡 ) according to the equation ke(t) = 6.5 + 3 tanh [(t − 260)/30], where time t is expressed in min. These sigmoidal evolutions, which also could be obtained by using logistic equations, are chosen so as to yield a delay in the rise of ke respective to the rise in σ, as observed in the experiments (26). The combined increase in σ and ke corresponds to the developmental path 3 shown in Fig. Fig.1 1 it brings the signaling system successively through the regions of nonexcitability (S), excitability (E), autonomous oscillations (O), and again excitability (E).
Distribution of starting times grouped in 10-min intervals, generated according to Eq. 3 for Δ = 25 min. (Inset) Time evolution of the fraction of cells capable of relaying a suprathreshold pulse of cAMP. This curve, to be compared with the experimental curve (see Fig. 1 in ref. 27), is obtained by calculating the cumulated number of cells entering domain E on path 3 (see Figs. Figs.1 1 and and4), 4 ), for the distribution of starting times shown in the present figure.
Fully developed spiral waves of cAMP triggered by the desynchronized progression of cells along the developmental path. Shown is the two-dimensional spatial distribution of cAMP (Upper) at indicated times (in min) resulting from the evolution of cells along the developmental path 3 in Fig. Fig.1 1 (Lower), associated with the time evolution of σ and ke prescribed in Fig. Fig.4. 4 . The distribution of starting times is given in Fig. Fig.5. 5 . Here again each circle represents a 5% fraction of the total amount of cells considered. Parameter values are as in Fig. Fig.3 3 the desynchronization factor Δ is equal to 25 min. The color scale indicates the level of the normalized concentration of extracellular cAMP, γ.
We have presented a developmentally based scenario for the onset of spiral waves of cAMP in the course of D. discoideum aggregation. The mechanism relies on the desynchronized evolution of cells on a developmental path that corresponds to continuous changes in enzyme activities after starvation, which bring about transitions between distinct modes of cAMP signaling. We have compared three different types of developmental paths and showed that only one of these, which corresponds to the observed increase in adenylate cyclase and phosphodiesterase, allows the formation of stable, fully developed spiral waves. These spirals form spontaneously without any kind of external perturbation (e.g., random firing of cAMP pulses) or unnatural intervention (breaking up waves). Our results indicate that of key importance for the triggering of spiral waves is the creation of defects in concentric waves, due to the combined effects of the temporal evolution of the biochemical parameters and of the desynchronization of these biochemical changes among different cells, which allows the simultaneous presence in the medium of nonexcitable, excitable, and oscillating cells. An additional requirement is that cells should follow the appropriate developmental path crossing successively the nonexcitable, excitable, and oscillatory states and return finally to an excitable state, as observed in the experiments.
The comparison of Figs. Figs.3 3 and and6 6 suggests that the formation of fully developed spirals is favored by the eventual return of the signaling system to an excitable state. Another contributing factor is the desynchronization of cells measured by parameter Δ (see Fig. Fig.3). 3 ). In Fig. Fig.6, 6 , spirals can be obtained even with a relatively small value of Δ = 25 min stronger desynchronizations would favor the creation of a larger number of smaller spirals. For this value of Δ, the time required for all cells to go from the nonexcitable to the excitable state is of the order of 4.6 Δ, i.e., about 120 min (see Fig. Fig.5 5 Inset). This value matches the observed time required for the acquisition of the relaying competence in aggregating Dictyostelium cells (27). It should be compared with the duration of the cell cycle, which is about 8 hr in axenic conditions (29) and shorter, of the order of 4 hr, when cells are grown in a bacterial medium (32). In contrast, the larger values of Δ, of the order of 100 min, yielding less developed spirals in Fig. Fig.3 3 for the developmental path 1, would yield a much larger value, of the order of the cell cycle length, i.e., 8 hr, for the time required for development of the relaying competence in the whole cell population. The choice of a value Δ = 25 min in Figs. Figs.5 5 and and6 6 also holds with another set of experimental results. Indeed Fig. Fig.5 5 indicates that it takes about 1 hr for 90% of the cells to enter the autonomous oscillatory domain this agrees with the observation that there is a cell-cycle-dependent 1-hr delay between cells that are the fastest and the slowest to produce cAMP oscillations (30).
The above discussion raises the question as to whether there exists an optimal range for cell desynchronization in regard to the formation of spirals. If the formation of large spirals appears to be favored from an evolutionary point of view, because they result in larger cell aggregates, smaller values of Δ should prove more effective. However, spirals fail to develop from concentric waves when desynchronization is too low, because no defects appear in these conditions. The balance between these two antagonistic effects points to the existence of an optimal range of Δ values.
The above scenario for the onset of spirals includes the effect of the inhibitor of phosphodiesterase considered by Pálsson and Cox (15), when considering that this inhibitor yields a lower effective value of ke early after starvation. Further addition of the inhibitor would correspond to bending the path 3, which more efficiently produces spirals, toward the left initially results similar to those shown in Fig. Fig.6 6 are obtained in these conditions. In a mutant lacking the phosphodiesterase inhibitor, a large number of small concentric waves rather than spirals are observed and appear later (E. Pálsson and E.𠂜. Cox, personal communication). In the model, accordingly, numerical simulations indicate that concentric waves are favored over spirals and form later when path 3 is shifted to the right, as a result of the absence of the inhibitor.
As indicated above, our results have been obtained for a cell density such that about 10 cells occupy one element of the spatial grid. Implicit in our description is the assumption that cells in this group behave as being synchronous in their development after starvation. Such a local synchronization could result from the fact that small groups of cells close to each other are almost certainly clonally related recently and therefore have undergone their last division at more or less the same time. Moreover, the short-range action of a differentiation-inducing factor secreted by starving cells favors the synchrony of development in their neighborhood (33).
Preliminary simulations with a finer spatial grid, which allows us to consider the evolution of single cells at the expense of increased computer time, yield similar results as to the effect of desynchronization on the onset of spiral waves. However, it is more difficult first to generate waves and, in a second time, to break them to obtain spirals if all cells are desynchronized. Small clumps of coordinated cells, desynchronized with respect to other such clumps, are more prone to overcome the leveling by diffusion of the spatial inhomogeneities that initiate waves and later nucleate the defects needed for spiral formation. A finer spatial grid further allows us to address the effect of cell density if each element of such a grid contains several cells (considered as synchronous) and the chemical reaction terms in the kinetic equation (Eq. 1c) for extracellular cAMP are multiplied by a factor related to the ratio of intracellular to extracellular volumes. In agreement with experimental observations (34), numerical simulations indicate that increasing cell density favors the formation of spirals.
Many studies of the formation of spiral patterns in chemical systems (35) have focused on the role of spatial heterogeneities (including obstacles) in breaking wavefronts so that their loose ends may curl into spirals. The mechanism outlined here for the breaking of wavefronts and subsequent formation of spirals in Dictyostelium cells is different in that it combines spatial heterogeneity with the temporal evolution of dynamical properties of the medium.
The present analysis brings to light the possible role played by the desynchronization of the biochemical changes induced after starvation in triggering the formation of spiral waves of cAMP. The model predicts that better synchronization of cells, e.g., through temperature shift (27, 38), release from nocodazole block (30), or dilution of stationary phase cells into growth medium (29), should favor the appearance of concentric waves over spirals. Desynchronization may be but one important factor for the emergence of spiral patterns in Dictyostelium, together with other contributing factors, including cell movement. Our results show how the concept of developmental path, which accounts for the sequential transitions in dynamic behavior of the cAMP signaling system in the course of time, ties in with the selection of a spatial pattern in the form of spirals of cAMP.
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Multicellular organisms consist of a variety of differentiated cells, and their differentiation processes must be tightly regulated to ensure their proper functions errors that occur during the differentiation process may induce fatal defects in organisms. Thus, understanding the regulatory mechanisms that determine cell lineages is a fundamental question for the fields of biology and medicine (Avior, Sagi, & Benvenisty, 2016 Castanon & González-Gaitán, 2011 Zakrzewski, Dobrzynski, Szymonowicz, & Rybak, 2019 ). At the beginning of the cell differentiation process, different cell types appear stochastically within a genetically identical cell population, which is known as the “salt and pepper” model and has been observed in various organisms, such as nematode worms, flies and mice (Chazaud, Yamanaka, Pawson, & Rossant, 2006 Miller, Seymour, King, & Herman, 2008 Schnabel et al., 2006 ). In such stochastic differentiation, non-genetic cellular heterogeneity, which arises from fluctuations of intrinsic and extrinsic factors, appears to be a key factor in the determination of cell fates. Intercellular variations in gene expression, metabolism and responses to cellular signals have been proposed to be intrinsic factors that affect cell fate (Elowitz, Levine, Siggia, & Swain, 2002 Evers et al., 2019 Raser & O’Shea, 2004 Yamanaka & Blau, 2010 ). Among these potential factors, metabolism appears to play a significant role in cell fate decisions. Increasing evidence has indicated that the activity levels of the mitochondria, important organelles associated with metabolism, play a role in the differentiation of human cells (Buck et al., 2016 Khacho et al., 2016 ). However, the critical factors that determine cell fate remain unknown.
The cellular slime mold Dictyostelium discoideum is an amoebozoa and represents a good model organism for studying relationships between cellular heterogeneity and cell differentiation during the development of multicellular organisms. Amoeboid cells continue to proliferate under nutrient-rich conditions (vegetative phase). Upon starvation, amoeboid cells initiate the process of multicellular development, differentiating into 2 major cell types: stalk cells and spore cells (Figure 1a). In the early stages of development, amoeboid cells move collectively toward extracellular cAMP oscillations, originated from an aggregation center, to form a multicellular mound. Cells that enter the mound phase begin to differentiate into stalk or spore progenitor cells, called prestalk and prespore cells, respectively, which arise stochastically in a salt and pepper fashion. Prestalk cells are sorted to the top side of the mound, forming the tip region, which later forms the anterior region of the migrating body (slug), whereas prespore cells constitute the posterior region of the slug. In the process of fruiting body formation, prestalk cells differentiate into stalk cells, penetrating into the prespore region of the slug. Spore cells generating progenies are moved to the top of the fruiting body through the support of stalk cells (Maeda, Inouye, & Takeuchi, 1997 ).
In the developmental process of D. discoideum, non-genetic cellular heterogeneities, which are generated by differences in intracellular calcium concentrations, cell cycles and metabolism levels, have been suggested to play significant roles in cell fate decisions (Baskar, Chhabra, Mascarenhas, & Nanjundiah, 2000 Cubbit, Firtel, Fischer, Jaffe, & Miller, 1995 Katz & Bourguignon, 1974 Leach, Ashworth, & Garrod, 1973 Maeda, 2005 , 2011 Maeda, Ohmori, Abe, Abe, & Amagai, 1989 Saran, Azhar, Manogaran, Pande, & Nanjundiah, 1994 Tasaka & Takeuchi, 1981 Thompson & Kay, 2000 Zada-Hames & Ashworth, 1978 ). Similar to humans and other higher eukaryotes, mitochondrial activity appears to be crucial when switching from growth to differentiation in the cellular slime mold. Furthermore, previous studies have reported that cell differentiation can be regulated by the presence or absence of glucose, which is an essential component of the glycolysis pathway, in the culture medium (Leach et al., 1973 Tasaka & Takeuchi, 1981 Thompson & Kay, 2000 ). These factors generating heterogeneities reflect cell conditions in the vegetative phase, implying that cell lineages are determined prior to differentiation according to the heterogeneities present in the vegetative phase.
In fact, a recent study has shown that cell fates in D. discoideum cells are predetermined in the vegetative phase, prior to multicellular formation, and can be predicted by expression levels of the omt12 gene (Kuwana, Senoo, Sawai, & Fukuzawa, 2016 ). Vegetative cells expressing high levels of omt12, which are termed as pstV A cells in Kuwana et al. ( 2016 ), are destined to differentiate into stalk cells (designated as “stalk-destined” cells, hereafter), whereas vegetative cells expressing low levels of omt12 are destined to differentiate into spore cells (designated as “spore-destined” cells, hereafter). However, omt12 is merely a marker gene, and changes in its expression levels do not influence the cell fates (Kuwana et al., 2016 ). The identities of the factors that drive differentiation of stalk-destined cells (expressing high omt12 levels) and spore-destined cells (expressing low omt12 levels) remain unknown.
To address this question, we first carried out RNA sequencing (RNA-seq) analyses to examine differences in gene expression levels between stalk-destined and spore-destined cells sorted by flow cytometry based on omt12 expression levels. The subsequent RNA-seq analysis and luciferase assay suggested that differences in ATP levels may be a factor that affects differentiation. We next monitored intracellular ATP levels during differentiation, using ATP sensor probes that we optimized for use in Dictyostelium. We also investigated roles of ATP in D. discoideum differentiation by using specific inhibitors of ATP production.
Cellular events in dedifferentiation
Dedifferentiation is a multi-stage process
During and after the erasure event, dedifferentiation can be described by the progression of several specific cellular events (Fig. 2). First, during the erasure event, cells lose the ability to release a chemoattractant ( Varnum & Soll 1981 ). Shortly after, in parallel with erasure stabilization, cells also lose cAMP-directed chemotaxis ( Varnum & Soll 1981 ). After erasure stabilization, cells retain cAMP-stimulated non-directed motility and ethylenediaminetetraacetic acid (EDTA)-resistant adhesion, mediated by CsA/gp80 ( Finney et al. 1979 ), for a few hours, and then these characteristics are also lost in distinct steps ( Soll & Mitchell 1982 Finney et al. 1983 ). Finally, after several hours in nutrient media, cells begin DNA synthesis and eventually begin to divide and resume normal growth-stage behaviors, indicating completion of dedifferentiation ( Takeuchi & Sakai 1971 Katoh et al. 2004 ).
During dedifferentiation, a shift in protein and transcript levels occurs, accompanied by changes in cellular events, chemoattractant release and response, adhesion, and motility. Following dedifferentiation, normal vegetative growth behaviors resume.
Many of the above cellular events require de novo protein synthesis. For example, prior to or after the erasure event, the addition of cycloheximide, an inhibitor of the erasure event ( Soll & Waddell 1975 Waddell & Soll 1977 ), inhibits the loss of EDTA-resistant adhesion completely. Even after the stabilization period, cycloheximide still partially inhibits this loss ( Soll & Mitchell 1982 ). Protein synthesis inhibitors thus interfere with more than just the erasure stage of dedifferentiation: Several cellular events during dedifferentiation also likely require de novo synthesis of specific molecules.
Changes at the protein level during dedifferentiation
At the protein level, Takeuchi & Sakai (1971) analyzed dedifferentiation by immunohistochemistry, quantifying the detection of a prespore-specific substance using a fluorescent-conjugated–anti-spore serum. When wild-type Dictyostelium NC4 cells were disaggregated from the slug stage of development and incubated in standard salt conditions ( Bonner 1947 ), the staining of the prespore-specific substance gradually decreased within 5 h of dedifferentiation. This process was sensitive to temperature, cycloheximide, the transcriptional inhibitor actinomycin D, and specific concentrations of cAMP (1 mmol/L but not 100 nmol/L). However, the presence or absence of bacteria had no effect.
Finney et al. (1987) further studied protein abundance, using 2-D gel electrophoresis to analyze 33 growth- and 53 development-associated polypeptides during dedifferentiation. These authors reported that most of the vegetative proteins (85%) increased dramatically within the first 5 h following disaggregation, with some (18%) increasing in abundance during the pre-erasure period, most of them (61%) increasing around the time of the erasure point, and only a few (9%) not increasing at all during the time of analysis. In contrast, most of the developmental proteins examined (77%) started to decrease in abundance dramatically within the first 5 h, especially in the pre-erasure-point period, and again only a few of the examined proteins did not decrease within the time of analysis. These authors also showed that the synthesis of one growth-associated polypeptide, V28, occurs concomitantly with the erasure event, is sensitive to the same concentration of cAMP that inhibits the erasure event, and is blocked by redevelopment.
Changes at the transcriptional level during dedifferentiation
In 2004, we used microarray technology to examine nearly 8000 targets including vegetative and developmental cDNAs and genomic DNAs ( Van Driessche et al. 2002 ), finding that global transcriptional changes occur early during dedifferentiation (Fig. 2) ( Katoh et al. 2004 ). Regardless of at what point developing cells are disaggregated, downregulation of about 700 developmental genes begins during the first 1 h of incubation in nutrient medium, and transcription of 1300 vegetative genes also begins during this time (representing changes in expression for 26.7% of the microarray targets). We also proposed that the dedifferentiation process could be divided into three phases based on observed transcriptional changes (Fig. 3) ( Katoh et al. 2004 ). Phase I is the period preceding the above described transcriptional changes, and this 1-h phase is consistent regardless of at what stage cells are prompted to dedifferentiate (developing cells were disaggregated from the aggregate, slug, and Mexican hat stages). Next, the above described global transcriptional change of about 2000 genes occurs in phase II, which encompasses the breadth of transcriptional change. The duration of this phase is dependent on the stage at which developing cells are prompted to dedifferentiate – more developed cells need more time to complete this period (Fig. 3). Phase III consists of a gap between the time when vegetative transcripts have accumulated to the level of that in vegetative cells and the time when DNA synthesis and subsequent cell division are resumed (Fig. 3) ( Katoh et al. 2004 ).
The three phases in dedifferentiation are based on global transcriptional changes. The length of time required for cells to complete dedifferentiation phase II (consisting of the bulk of transcriptional changes) is dependent on the stage at which developing cells were disaggregated and exposed to nutrients.
We also identified 272 transcripts that are upregulated during phase II of dedifferentiation ( Katoh et al. 2004 ). Almost all of these transcripts are also upregulated during development, suggesting that some developmental genes have a role in phase II of dedifferentiation as well, perhaps allowing cells the plasticity to adjust to rapid environmental changes. However, we also identified 122 transcripts that were regulated only during dedifferentiation and not during development. Using gene ontology (GO), we analyzed these two groups and found enrichment of the phase II upregulated genes in GO categories including RNA metabolism, energy reserve metabolism, protein transport, ribosome biogenesis, and two-component signal transduction. In contrast, the dedifferentiation-specific subset belonged to GO categories such as response to biotic stimulus, ubiquitin cycle, amino acid metabolism, nitrogen metabolism, and protein targeting ( Katoh et al. 2004 ).
Enzymatic Properties of the Two D -Ser Degradation Enzymes DSD and DAO
The D. discoideum genome encodes SR (DDB0230209), and unlike mammals two putative D-Ser degradation enzymes: DAO (DDB0238432), and DSD (DDB0305709). The DDB0238432 protein exhibit 33 and 32% sequence identity with human DAO and D-aspartate oxidase, respectively. The DDB0305709 protein share 21.5% sequence identity with DSD of S. cerevisiae (Dsd1p). To obtain insight into the significance of D-Ser and its putative metabolic enzymes in D. discoideum, we analyzed the enzymes’ properties.
The putative DSD (DDB0305709 protein) was expressed in E. coli using the pET vector system and purified to homogeneity by Ni-affinity chromatography. The UV-visible spectrum of the DDB0305709 protein displayed an absorbance maximum at 420 nm, which is characteristic of the formation of Schiff-base between PLP and amino acid (Figure 1A), indicating PLP-binding ability of the protein. The DDB0305709 protein exhibited D-Ser dehydration activity in an optimal pH of 8.0. The kcat and Km values for the D-Ser dehydration catalyzed by the enzyme were 2.6 s -1 and 0.19 mM, respectively (Table 1). D-Thr and β-Cl-D-alanine (a D-Ser analog in which the OH group is replaced with a Cl) are known to be substrates of Dsd1p. The DDB0305709 protein also exhibited reactivity toward D-Thr and β-Cl-D-alanine with a catalytic efficiency of 0.9 and 5.3%, respectively, when compared to that of D-Ser (Table 1). As with Dsd1p (Ito et al., 2008) and chicken DSD (Tanaka et al., 2011), the DDB0305709 protein required Zn 2+ for maximal D-Ser dehydration activity: D-Ser dehydration activity was greatly decreased by the EDTA-treatment, and was restored by adding Zn 2+ (Figure 1B). Addition of other divalent metal ions such as Mg 2+ , Ca 2+ , Mn 2+ , Ni 2+ , and Fe 2+ to the EDTA-treated enzyme did not restore enzyme activity (Figure 1B). These results indicate that the DDB0305709 protein, hereafter DSD, is a PLP- and Zn 2+ -dependent DSD.
FIGURE 1. Characterization of recombinant D-Ser dehydratase (DSD) and D-amino acid oxidase (DAO) of Dictyostelium discoideum. (A,B) UV-vis spectra of purified DSD (A) and DAO (B). (C) Relative activities of Ethylenediaminetetraacetic acid (EDTA)-treated DSD in the presence of various metal ions. Activity of EDTA-treated DSD was assayed in the presence of 10 mM D-Ser and various concentrations (10μ, 100,μ and 1000 μM) of metal ion. Data are represented as the average of the duplicate measurements. (D) Relative activity of deamination activity of DAO with various D-amino acids. Purified DAO was incubated with 10 mM of D-amino acid and the resultant H2O2 was quantified in the presence of horseradish peroxidase (HRP), TOOS, and 4-aminoantipyrine (4-AAP) at 550 nm. Data are represented as the average of the duplicate determinations.
TABLE 1. Kinetic parameters of recombinant SR, DSD, and DAO of Dictyostelium discoideum.
We also constructed an expression system for the putative D. discoideum DAO (DDB0238432). The purified DDB0238432 protein displayed absorption maxima at around 360 and 450 nm, indicating that it possesses an oxidized form of the flavin cofactor (Figure 1C). The DDB0238432 protein deaminated various D-amino acids. Among them, D-Ala was the best substrate: kcat and Km values for the D-Ala oxidase activity were 0.53 s -1 and 0.78 mM, respectively (Table 1). Little reactivity was observed for D-Asp and D-Glu (Figure 1D). These results demonstrated that DDB0238432 encode functional DAO. D-Ser was not a good substrate for DAO (Figure 1D), with kcat and Km values of 0.05 s -1 and 6.1 mM, respectively (Table 1).
It has become clear that D. discoideum possesses three functional enzymes capable of degrading D-Ser: DSD, DAO, and SR. SR was characterized previously (see Ito et al., 2012b, 2013b Table 1). As judged by kcat/Km values, the D-Ser degradation activity of DSD is several hundred-fold higher than that of the other two enzymes (Table 1). These results indicate that, unlike in mammals, which use DAO as the primary D-Ser degradation enzyme, D-Ser is probably degraded by DSD in D. discoideum.
DSD Mutation Abolished D -Ser Degradation Activity in the Cell
To examine the physiological significance of DSD in D-Ser metabolism, we constructed a dsd-null mutant (㥍SD). In the mutant strain, dsd was replaced by the blasticidin S resistance gene (Supplementary Figure S1). The successful generation of dsd-null cells demonstrated that dsd is not required for the unicellular growth of D. discoideum under the conditions tested.
We first determined the D- and L-Ser degradation activities in WT and dsd-null cells. D- or L-Ser was incubated with cell-free extract of WT or dsd-null cells, and the rates of D- or L-Ser decomposition were quantified. Time-dependent decrease of D-Ser was observed only with the cell-free extract of WT (Figure 2A). The average D-Ser decomposition rate during 7.5 h incubation was 15 nmol.h -1 .mg protein -1 with the WT cell-free extract. In contrast, the rate was lower than the detection limit (π.2 nmol.h -1 .mg protein -1 ) with the cell-free extract of dsd-null cells (Table 2 and Figure 2A). Similar experiments were performed using multicellular-stage cells (cells at 12 h after induction of development). Again, the cell-free extract of WT exhibited D-Ser degradation activity, while that of dsd-null cells did not (data not shown).
FIGURE 2. Ser degradation activity of Wild-type (WT) and dsd-null strain. (A) D-Ser degradation activity in the cell-free extract of WT () and dsd-null cells (). Data are represented as the mean ± SD (n = 3). (B) Identification of the keto acid product formed from D-Ser by the cell-free extract of WT (3) and dsd-null cells (4). Elution profiles of the MBTH-derivatized pyruvate (1) and hydroxypyruvate (2) are also shown as controls. α-Ketoglutarate was used as an internal standard for the derivatization of keto acids as described previously (Ito et al., 2008). Samples after 7.5 h incubation were used for this analysis.
TABLE 2. Ser degradation activity in the cell-free extract of WT, dsd-null, and 㥍SD/dsd + .
In mammals, DAO plays important roles in D-Ser decomposition. Our data suggest that the primary enzyme that degrades D-Ser in D. discoideum is DSD. From D-Ser, DSD generates pyruvate, while DAO produces hydroxypyruvate as the keto acid. To examine the contribution of DAO in D-Ser metabolism in D. discoideum, the keto acid generated from D-Ser was identified. As shown in Figure 2B, with the WT cell-free extract, the keto acid produced from D-Ser was pyruvate. No pyruvate formation was observed with dsd-null cells. Hydroxypyruvate were not formed with both WT and dsd-null cells (Figure 2B). From the above data, we conclude that DSD is responsible for D-Ser degradation in D. discoideum.
L-Ser degradation activity in cell-free extracts of WT and dsd-null cells was also analyzed. The L-Ser degradation activity was unaffected by the dsd mutation (Table 2). Little D-Ser was formed from L-Ser, indicating that SR does not play a significant role in L-Ser degradation under the tested conditions.
Effects of dsd Mutation on Growth and Development
To examine the effects of the dsd mutation on unicellular growth, WT and dsd-null cells were grown on 5LP-agar plates along with Klebsiella aerogenes, and the size of the plaques formed on the bacterial lawns was measured as an indicator of growth rate. In the absence of D-Ser, the rate of plaque growth was 0.23 cm/day with WT and 0.32 cm/day with dsd-null cells, indicating that dsd-null cells grew slightly faster than WT (Figure 3A). On the 5LP plates, dsd-null cells apparently formed fewer multicellular structures (such as slug, mounds, fruiting bodies) than WT did, as judged for both photographs at identical plaque diameters (appeared as dots, see panels a and b in Figure 3B). This indicates that the dsd mutation induces delayed initiation of multicellular development.
FIGURE 3. Growth and development of WT and dsd-null cells and the effect of exogenous D-Ser. D. discoideum cells were grown on Klebsiella aerogenes on 5LP agar plates containing indicated D-Ser concentrations. (A) The rates of plaque diameter increase of WT (closed bars) and dsd-null cells (open bars). Data are represented as the mean ± SD (n = 3). Asterisks indicate significant differences (Student’s t-test, P < 0.05). (B) Photographs of plaque of WT (Top) and dsd-null cells (Bottom) were taken when the plaque diameter reached approximately 1-cm (4 days in b,d, 5 days in a,c,e–i, 7 days in j). Multicellular structures are indicated as arrows. (C) D. discoideum cells were grown in HL5 liquid medium in the presence or absence of 10 mM D-Ser. Data are represented as the mean ± SD (n = 3).
To examine more precisely the effect of the dsd mutation on the developmental process, WT and dsd-null cells were grown axenically in HL5 medium, whereupon development on nitrocellulose membranes was induced by starvation. At 14 h after induction of development, WT cells were in late mound stage (tip-forming stage), but dsd-null cells were in aggregation and/or mound stages (Figure 4, panels a and b). WT cells formed slugs at 16 h (panel c) and fruiting bodies at 24 h (panel g). In contrast, in dsd-null cells, tip-forming aggregates were formed at 18 h development (panel f). These observations indicated that dsd-null cells show delay in development and apparently spend a prolonged period at the mound stage before proceeding with development. Interestingly, dsd-null cells formed fewer fruiting bodies than WT. After 72 h, the number of spores formed in dsd-null cells was 6 ± 2% of that in WT.
FIGURE 4. Developmental phenotypes of dsd-null mutant. WT (Top) and dsd-null cells (Bottom) were developed on the filter membrane at a density of 5.0 × 10 7 cells/cm 2 . The dsd-null cells exhibited aggregation delay and impaired spore formation efficiency compare to WT. Photographs of fruiting body were shown in the inset. Bars indicate 1 mm and 0.5 mm (inset). Experiments were conducted at least twice, and more than three times.
Accumulation of D -Ser in dsd-Null Cells and Its Effect on Growth and Development
Our data suggest that dsd-null cells probably accumulate D-Ser, potentially causing the developmental delay and the spore-formation defect. We therefore quantified the intracellular D-Ser levels in WT and dsd-null cells (Figure 5). D-Ser concentration was under the detection limit in WT cells at the unicellular phase (π.003 nmol/mg) (Figure 5 and Table 3). In dsd-null cells, it was 0.026 nmol/mg, demonstrating the accumulation of D-Ser in dsd-null cells. D-Ser concentrations during development were further examined in WT and dsd-null cells. D-Ser was not detected throughout development in WT. During the development of dsd-null cells, D-Ser was present in the cells at levels of 0.01𠄰.03 nmol/mg cells (Figure 5B). This indicates that D-Ser is likely able to influence all stages of development in the mutant strain.
FIGURE 5. Effect of dsd knockout on the intracellular Ser concentration. Intracellular amino acid concentrations of WT, dsd-null, and 㥍SD/dsd + were determined. (A) Representative chromatograms of intracellular amino acid analyses of WT, dsd-null, and 㥍SD/dsd + of unicellular phase cells. Peak of D-Ser derivative is indicated by arrow. The small peaks at Retention Time 23.5 min in WT and 㥍SD/dsd + samples are not for D-Ser derivative (because they were not decreased/disappeared by incubation with Dsd1p). (B) Intracellular D-Ser and L-Ser concentrations during development. WT and dsd-null cells were grown in HL5 medium and allowed to develop on a filter membrane by starvation. Cells were collected at different stages of development and the intracellular concentrations of D-Ser and L-Ser were quantified by HPLC. Intracellular D-Ser levels were under the detection limit in WT. Data are represented as the mean ± SD (n = 3).
TABLE 3. Intracellular serine levels of WT, dsd-null, and 㥍SD/dsd + .
Our data strongly suggest that the accumulation of D-Ser causes the dsd-null phenotypes. To examine whether D-Ser indeed shows inhibitory effects on D. discoideum development, cells were grown along with K. aerogenes on 5LP plates containing various concentrations of D-Ser the effect on growth and/or development was then examined. In the presence of 0.5 mM of D-Ser, no significant effect on growth or development was observed in WT (Figure 3). Delayed development and an inhibitory effect on unicellular growth of WT were observed when 25 mM D-Ser was added to the plates (Figures 3A,B panel i). In contrast, unicellular growth of dsd-null cells was dose-dependently inhibited by D-Ser, and no growth was observed with 25 mM D-Ser (Figures 3A,B panel j). In all conditions, dsd-null cells apparently formed fewer multicellular structures (such as slug, mounds, fruiting bodies) than WT did, as judged when plaque diameters reached 1 cm, and the number of multicellular structures decreased by increasing exogenous D-Ser (b > d > f > h, Figure 3B). This shows that the developmental delay was further pronounced by exogenous D-Ser in dsd-null cells. In addition, the impaired spore-forming ability of dsd-null cells was further pronounced by adding exogenous D-Ser. Spore numbers dropped to 1.3 ± 0.3% and 0.3 ± 0.1% of WT in the presence of 1 and 10 mM of D-Ser in the KK2 buffer used for the experiments, respectively. Growth assay was also conducted with the HL5 liquid medium in the presence or absence of 10 mM D-Ser. In the absence of D-Ser, the dsd-null cells grew slightly faster than WT in log-phase, but thereafter the increase of cell number was slowed down. The growth of WT was not affected by exogenous D-Ser. In contrast, the growth of dsd-null cells was significantly hampered by D-Ser (Figure 3C). These observations confirm that D-Ser inhibits and/or delays the unicellular growth, aggregation and spore-forming processes of D. discoideum.
Complementation of dsd
To demonstrate that the dsd-null phenotypes are indeed caused by the dsd mutation, we constructed a dsd complementary strain (㥍SD/dsd + ). In this strain, dsd was constitutively expressed under the control of the actin15 promoter by using the plasmid pDEX-RH. The cell-free extract of 㥍SD/dsd + unicellular ameba exhibited 312 nmol.h -1 .mg protein -1 of D-Ser degradation activity, corresponding to the 20-fold higher expression of dsd in 㥍SD/dsd + than in WT (Table 2). Intracellular amino-acid analysis demonstrated that the 㥍SD/dsd + strain does not accumulate D-Ser both in the unicellular (Figure 5A) and multicellular phase. On nitrocellulose membranes,㥍SD/dsd + cells developed apparently normally and formed fruiting bodies 24 h after induction of development (Figure 6A). Upon dsd overexpression, the efficiency of spore formation increased significantly (but not to WT levels). The data suggested that controlled expression of dsd is required for the full restoration of the phenotypes of dsd-null cells. The number of spores formed in 㥍SD/dsd + was 53 ± 10% of that in WT. The delayed initiation of development on 5LP-agar plates with K. aerogenes was rescued too (Figure 6). These results clearly demonstrate that the phenotypes observed in dsd-null cells are caused by the dsd mutation.
FIGURE 6. Effect of dsd expression in dsd-null cells on development. (A) WT and dsd-null cells both of which harbor empty vector pDEX-RH (WT/- and dsd-null/-) and 㥍SD/dsd + were developed on filter membrane at a density of 5.0 × 10 7 cells/cm 2 . Photographs were taken at indicated times. The numbers of spore were counted at 72 h after starvation. The total amount of spore formed in WT was regarded as 100%. (B) WT/-, dsd-null/-, and 㥍SD/dsd + were grown on 5LP plate with K. aerogenes at 22ଌ. Photographs were taken when the plaque diameter reached approximately 1-cm (4 days for WT/-, 3 days for dsd-null/-, and 㥍SD/dsd + ). All experiments were performed at least twice, and more than three times.
Dsd Mutation Affects the cAMP Signaling Relay
In early development of D. discoideum, cAMP plays important roles in intracellular and extracellular signaling (Konijn et al., 1967 Dinauer et al., 1980 Williams et al., 1984 Mahadeo and Parent, 2006). cAMP is required for the chemotaxis of Dictyostelium cells and for the activation of the cAMP receptor CarA. CarA is expressed in early development to initiate a signaling cascade for programmed development. The cascade leads to a rapid synthesis of cAMP by the adenylyl cyclase AcaA and to cAMP secretion. This is followed by cAMP degradation mediated by the secreted cAMP phosphodiesterase PsdA (Faure et al., 1990).
We hypothesized that the developmental delay in dsd-null cells may be caused by a defect and/or perturbation in the cAMP signaling relay. To examine this, the expression of the key cAMP signaling genes, carA and acaA was analyzed (Figures 7A,B). It is known that, in WT, the mRNA levels of carA and acaA peak at 4𠄸 h after starvation (Faure et al., 1990 Narita et al., 2014). Indeed, in WT, the mRNA levels of acaA peaked at 8 h and increased 50-fold after starvation (Figure 7A). In dsd-null cells, expression of acaA peaked at 8 h but increased only 17-fold. The same was observed for carA. In WT, mRNA levels of carA increased sixfold at 8 h, but only threefold in dsd-null cells (Figure 7B). Although no significant differences were observed, the pdsA expression was slightly delayed. The expression of pdsA was peaked at 4 h in WT and at 8 h in dsd-null cells (data not shown). In addition, we found that dsd is expressed constitutively during development, and is most highly expressed in early development (Figure 7C). This may reflect the significance of D-Ser regulation particularly in the initiation of development. The decreased expression of the key cAMP-signaling genes during aggregation may serve to explain the delayed aggregation process in dsd-null cells. Our data suggest that DSD plays an important role in the regulation of cAMP-signaling genes in early development in D. discoideum.
FIGURE 7. Expression patterns of cAMP signaling genes in dsd-null mutant and those of dsd and dao in WT. WT and dsd-null cells growing exponentially in HL5 medium were developed on buffer-saturated filters. Total RNA was extracted at the indicated time points and the expression levels of acaA (A), carA (B), dsd (C), and dao (D) were quantified by qRT-PCR. The mRNA level of each gene was normalized by the expression of ig7 mRNA in the respective sample. Values are represented as the mean ± SD (n = 3). Asterisks indicate significant differences between WT and dsd-null cells (Student’s t-test, P < 0.05).
In the presence of nutrients, the cellular slime mold Dictyostelium discoideum grows as free living single-celled amoebae, but upon starvation these amoebae aggregate into a multicellular organism that progresses through a motile slug stage to form a spore mass or sorus supported by a stalk (Kessin, 2001). Aggregation occurs in response to cyclic AMP (cAMP), which is synthesized and secreted soon after the onset of starvation. cAMP binds to cell surface cAMP receptors, resulting in the dissociation and activation of a heterotrimeric G protein, and this in turn leads to signaling through various downstream effectors that mediate chemotaxis to cAMP and the cAMP relay, the process by which the signal is passed throughout the cell population. The chemotactic response involves the activation of phosphatidylinositol 3-kinase (PI3K) and protein kinase B (PKB Iijima et al., 2002 Manahan et al., 2004 Postma et al., 2004), whereas the cAMP relay involves the activation of adenylyl cyclase (ACA Parent and Devreotes, 1996).
Ras proteins are monomeric, small GTPases that function as molecular switches, cycling between active GTP-bound and inactive GDP-bound states (Bourne et al., 1991). Activation is regulated by guanine-nucleotide-exchange factors (GEFs), and inactivation is regulated by GTPase-activating proteins (GAPs) that stimulate the hydrolysis of the bound GTP to GDP (Boguski and McCormick, 1993). The Ras superfamily can be divided, on the basis of sequence comparisons, into several distinct subfamilies, one of which is the Ras subfamily (Colicelli, 2004). The human Ras subfamily consists of 36 distinct gene products that can be divided into several groups (Mitin et al., 2005). The search for downstream effectors has revealed some specificity but also an enormous complexity of overlapping functions, even between members of the different groups within the subfamily (Rodriguez-Viciana et al., 2004). Despite a relatively small genome, Dictyostelium possesses a large number of Ras subfamily GTPases (Weeks et al., 2005), and there is evidence that each protein performs a distinct function (Weeks and Spiegelman, 2003). Dictyostelium therefore provides a useful experimental model for the study of Ras function.
The initial evidence for a role of Ras signaling pathways in regulating the Dictyostelium aggregation process was the disruption of a gene encoding a RasGEF, RasGEFA, which prevented aggregation (Insall et al., 1996). Direct evidence for a role for Ras came with the disruption of the rasC gene, which produced cells that failed to aggregate (Lim et al., 2001). rasC null cells exhibited reduced activation of ACA and reduced phosphorylation of PKB in response to cAMP, suggesting a role for RasC in the signal transduction pathways that regulate both the cAMP relay and chemotaxis.
We recently found that both RasC and RasG were activated in response to cAMP, suggesting a possible role for RasG in the aggregation process (Kae et al., 2004). However, the properties of the two previously isolated rasG null strains, IR15 and IR17, had suggested that the major role for RasG was in Dictyostelium growth and other vegetative cell functions (Tuxworth et al., 1997 Khosla et al., 2000), and the only defect observed in development was a slight but inconsistent delay in the onset of aggregation (R. H. Insall, personal communication). In view of the variable defects in development and the relative instability of the previously described rasG null strains (Khosla et al., 2000 R. H. Insall and G. Weeks, unpublished observations), new rasG null strains were generated, to study more definitively the possible role of RasG in early development. For comparison, we also generated a rasC rasG double null and rasC null strains in an isogenic background. Studies of these strains have revealed that the branches of the bipartite cAMP signal-transduction pathway depend primarily on either RasG or RasC, although there is also some overlap of Ras protein function.
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The cellular slime molds exist as individual amoeboid cells that periodically aggregate. The individual amoebe can be seen aggregating in Figure (PageIndex<1>)(a). The aggregate then forms a fruiting body (Figure (PageIndex<2>)) that produces haploid spores. One cellular slime mold, Dictyostelium discoideum, has been an important study organism for understanding cell differentiation, because it has both single-celled and multicelled life stages, with the cells showing some degree of differentiation in the multicelled form. Watch Video (PageIndex<1>) to see how these individuals aggregate into a single fruiting body.
Video (PageIndex<1>): Watch the strange behavior of the cellular slime mold Dictostelium discoideum as individual amoebae respond to an aggregation signal (cAMP), form a mobile slug, and eventually produce a stalked fruiting structure and spores. Sourced from YouTube.
The organisms in this group have a complex life cycle (Figure (PageIndex<3>)) during the course of which they go through unicellular, multicellular, spore producing, and amoeboid stages. Thousands of individual amoebae aggregate into a slimy mass - each cell retaining its identity (unlike plasmodial slime molds). The aggregating cells are attracted to each other by the cyclic AMP (cAMP) that they release when conditions become stressful, such as a depletion in food. Individual amoebae respond to the chemical signal by moving to areas of higher cAMP concentration (chemotaxis), eventually aggregating into a single slug. The slug can respond to moisture and light gradients, navigating to a good spot for spore production. Some cells in the slug contribute to a 2&ndash3-millimeter stalk, drying up and dying in the process. Cells atop the stalk form an asexual fruiting body that contains haploid spores. The spores are disseminated and can germinate if they land in a moist environment.
Figure (PageIndex<3>): Dictyostelium life cycle (text from the original figure caption). "(A) During the growth phase of development, amoeboid cells feed on bacteria and replicate by binary fission. The development cycle is initiated upon resource depletion, and aggregation occurs when starving cells secrete cyclic AMP to recruit additional cells (B). The aggregating cells organize to form the mound stage enclosed within an extracellular matrix composed of cellulose and mucopolysaccharide (26) (C) and continue to develop into the standing slug (D). Depending on its environment, the standing slug either falls over to become a migrating slug that moves toward heat and light (e) or proceeds directly to the culmination stages (F) that ultimately produce the fruiting body, which consists of a spore-containing structure, the sorus, held aloft by a stalk of dead cells (g). Spores are released from the sorus and germinate into growing cells (H). Under optimal conditions, the developmental cycle takes around 24 h. If the slug forms underground, it migrates toward the surface to maximize spore dissemination. To protect itself from infection during migration, the slug possesses a rudimentary immune system comprising phagocytic sentinel cells. These cells move throughout the slug, take up bacteria and toxins, and are shed along with extracellular matrix as the slug moves (e). In response to bacteria, sentinel cells release extracellular traps, derived from mitochondrial DNA, via an unknown mechanism involving NADPH oxidase (NOX)-generated reactive oxygen species (ROS) and TirA, a soluble protein containing a toll/interleukin 1 receptor domain (i)." Figure sourced from the publication Eat Prey, Live: Dictyostelium discoideum as a model for cell-autonomous defenses. Dunn et al., (2018). CC BY 4.0 DOI: 10.3389/fimmu.2017.01906
Dictyostelium Aggregation How can pattern-formation inform cell biology - PowerPoint PPT Presentation
Title: Gradient Sensing Author: Herbert Levine Last modified by: Herbert Levine Created Date: 2/17/2005 9:17:52 PM Document presentation format: On-screen Show &ndash PowerPoint PPT presentation
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