In complex multicellular organisms, different cells specialise to execute different functions. These functions can be generally classified into two kinds: reproductive and vegetative. Cells performing reproductive functions contribute to the next generation of organisms, while cells performing vegetative function contribute to sustaining the organism itself. In unicellular species and simple multicellular colonies, these two kinds of functions are performed at different times by the same cells – specialization is temporal. In more complex multicellular organisms, specialization transforms from temporal to spatial (Mikhailov et al., 2009), where groups of cells focused on different tasks emerge in the course of organism development.

Typically, cell functions are changed via differentiation, such that a daughter cell performs a different function than the maternal cell. The vast majority of metazoans feature a very specific and extreme pattern of cell differentiation: any cell performing vegetative functions forms a somatic lineage, that is, producing cells performing the same vegetative function – somatic differentiation is irreversible. Since such somatic cells cannot give rise to reproductive cells, somatic cells do not have a chance to pass their offspring to the next generation of organisms. Such a mode of organism development opened a way for deeper specialization of somatic cells and consequently to the astonishing complexity of multicellular animals. Outside of the metazoans – in a group of green algae Volvocales serving as a model species for evolution of multicellularity – the emergence of irreversibly differentiated somatic cells is the hallmark innovation marking the transition from colonial life forms to multicellular species (Kirk, 2005).

While the production of individual cells specialized in vegetative functions comes with a number of benefits (Grosberg and Strathmann, 2007), the development of a dedicated vegetative cell lineage that is lost for organism reproduction is not obviously a beneficial adaptation. From the perspective of a cell in an organism, the guaranteed termination of its lineage seems the worst possible evolutionary outcome for itself. From the perspective of an entire organism, the death of somatic cells at the end of the life cycle is a waste of resources, as these cells could in principle become parts of the next generation of organisms. For example, exceptions from irreversible somatic differentiation are widespread in plants (Lanfear, 2018) and are even known in simpler metazoans among cnidarians (DuBuc et al., 2020) for which differentiation from vegetative to reproductive functions has been reported. Therefore, the irreversibility of somatic differentiation cannot be taken for granted in the course of the evolution of complex multicellularity.

Terminal differentiation is a type of cell differentiation different from irreversible cell differentiation. Unlike irreversibly differentiated cells who are capable of cell division, terminally differentiated cells lose the ability to divide. Terminally differentiated cells often perform tasks too demanding to be compatible with cell division. For example heterocysts of cyanobacteria perform nitrogen fixation, which requires anaerobic conditions, therefore these cells are very limited in resources and do not divide. In the scope of this study, we do not consider terminal differentiation but focus on somatic cells that are able to divide while being part of an organism (or cell colony) but not able to grow into a new organism, that is, irreversible somatic differentiation.

The majority of the theoretical models addressing the evolution of somatic cells focuses on the evolution of cell specialization, abstracting from the developmental process how germ (reproductive specialists) and soma are produced in the course of the organism growth. For example, a large amount of work focuses on the optimal distribution of reproductive and vegetative functions in the adult organism (Michod, 2007; Willensdorfer, 2009; Rossetti et al., 2010; Rueffler et al., 2012; Ispolatov et al., 2012; Goldsby et al., 2012; Solari et al., 2013; Goldsby et al., 2014; Amado et al., 2018; Tverskoi et al., 2018). However, these models do not consider the process of organism development. Other work takes the development of an organism into account to some extent: In Gavrilets, 2010, the organism development is considered, but the fraction of cells capable of becoming somatic is fixed and does not evolve. In Erten and Kokko, 2020, the strategy of germ-to-soma differentiation is an evolvable trait, but the irreversibility of somatic differentiation is taken for granted. In Rodrigues et al., 2012, irreversible differentiation was found, but both considered cell types pass to the next generation of organisms, such that the irreversible specialists are not truly somatic cells in the sense of evolutionary dead ends. Finally, in Cooper and West, 2018 a broad scope of cell differentiation patterns has been investigated in the context of evolution of cooperation. However, irreversible somatic differentiation was not considered in the study. Hence, the theoretical understanding of the evolution of irreversibly differentiated somatic cell lines is limited so far.

In the present work, we developed a theoretical model to investigate conditions for the evolution of the irreversible somatic differentiation. In the model, we suppose there are two cell types: germ-role and soma-role, where only germ-role cells pass to the next generation of organisms while soma-role cells are responsible for vegetative functions. Both germ-role cells and soma-role cells can divide and they may switch to each other during growth. In our model, we incorporate factors including (i) costs of cell differentiation, (ii) benefits provided by presence of soma-role cells, (iii) maturity size of the organism. We ask under which circumstances irreversible somatic differentiation is a strategy that can maximize the population growth rate compared to strategies in which differentiation does not occur or somatic differentiation is reversible.

Model

We consider a large population of clonally developing organisms composed of two types of cells: germ-role and soma-role. The roles differ in the ability to survive beyond the end of the organism life cycle: soma-role cells die at the end, while germ-role cells continue to live. Each organism is initiated as a single germ-role cell. In the course of the organism growth, germ-role cells may differentiate to give rise to soma-role cells and vice versa, see Figure 1A,B. After $n$ rounds of synchronous cell divisions, the organism reaches its maturity size of $2^n$ cells. Immediately upon reaching maturity, the organism reproduces: germ-role cells disperse and each becomes a newborn organism, while all soma-role cells die and are thus lost, see Figure 1A. We assume that soma-role cells are capable to accelerate growth: an organism containing more somatic cells grows faster, so having soma-role cells during the life cycle is beneficial for the organism.

Figure 1

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To investigate the evolution of irreversible somatic differentiation, we consider organisms in which the functional role of the cell (germ-role or soma-role) is not necessarily inherited. When a cell divides, the two daughter cells can change their role, leading to three possible combinations: two germ-role cells, one germ-role cell plus one soma-role cell, or two soma-role cells. We allow all these outcomes to occur with different probabilities, which also depend on the parental type, see Figure 1B. If the parental cell had the germ-role, the probabilities of each outcome are denoted by $g_{gg}$, $g_{gs}$, and $g_{ss}$ respectively. If the parental cell had the soma-role, these probabilities are $s_{gg}$, $s_{gs}$, and $s_{ss}$. Altogether, six probabilities define a stochastic developmental strategy $D=(g_{gg},g_{gs},g_{ss};s_{gg},s_{gs},s_{ss})$. In our model, it is the stochastic developmental strategy that is inherited by offspring cells rather than the functional role of the parental cell.

To feature irreversible somatic differentiation, the developmental strategy must allow germ-role cells to give rise to soma-role cells ($g_{gg}<1$) and must forbid soma-role cells to give rise to germ-role cells ($s_{ss}=1$). All other developmental strategies can be broadly classified into two classes. Reversible somatic differentiation describes strategies where cells of both roles can give rise to each other: $g_{gg}<1$ and $s_{ss}<1$. In the strategy with no somatic differentiation, soma-role cells are not produced in the first place: $g_{gg}=1$, see Table 1.

Table 1

Class	$g_{gg}$	$s_{ss}$
Irreversible somatic differentiation	<1	= 1
Reversible somatic differentiation	<1	<1
No somatic differentiation	= 1	irrelevant

In our model, evolution of the developmental strategy is driven by the growth competition between populations executing different strategies – these populations able to produce more offspring and/or complete their life cycle faster gain a selective advantage. Specifically, we measure the fitness in the growth competition by the population growth rate in a stationary regime of exponential growth (Pichugin et al., 2017; Gao et al., 2019). The rate of population growth is determined by the number of offspring produced by an organism (equal to the number of germ-role cells at the end of life cycle) and the time needed for an organism to develop from a single cell to maturity (improved with the number of soma-role cells during the life cycle).

To obtain these growth rates, we simulate the process of the organism growth. Here, we assume that resource distribution among cells is coordinated at the level of the organism: Cells which need more resources will get more, such that cell division is synchronous. In our model, we consider synchronous cell division of organisms and our main results are dependent on this assumption. However, we shortly explore the effects of asynchronous cell division in Appendix G. Any organism is born as a single germ-role cell and passes through $n$ rounds of simultaneous cell divisions. Each round starts with every cell independently choosing the outcome of its division with probability of each outcome given by the developmental strategy ($D$). This step determines what composition will the organism have at the next round of cell division. Then, the length of the cell doubling round ($t$) is computed as a product of two independent effects: the differentiation effect $F_{\text{diff}}$ representing costs of changing cell roles (Gallon, 1992) and the organism composition effect $F_{\text{comp}}$ representing benefits from having soma-role cells (Grosberg and Strathmann, 1998; Shelton et al., 2012; Matt and Umen, 2016),

(1) $t=F_{\text{diff}}\times F_{\text{comp}}.$

Both $F_{\text{diff}}$ and $F_{\text{comp}}$ are re-calculated at every round of cell division.

The cell differentiation effect $F_{\text{diff}}$ represents the costs of cell differentiation. The differentiation of a cell requires efforts to modify epigenetic marks in the genome, recalibration of regulatory networks, synthesis of additional and utilization of no longer necessary proteins. This requires an investment of resources and therefore an additional time to perform cell division. Hence, any cell, which is about to give rise to a cell of a different role, incurs a differentiation cost $c_{g\to s}$ for germ-to-soma and $c_{s\to g}$ for soma-to-germ transitions (and double of these if both offspring take a role different from the parent), see Figure 1C. The differentiation cost is the averaged differentiation cost among all cells in an organism

(2) $F_{\text{diff}}=1+⟨c⟩=1+\frac{c_{s\to g}(N_{s\to gs}+2N_{s\to gg})+c_{g\to s}(N_{g\to gs}+2N_{g\to ss})}{N},$

where $N_{s\to gs}$ is the number of soma-roll cells that produce a germ-role cell and a soma-role cell in a cell division step. $N_{s\to gg}$, $N_{g\to gs}$ and $N_{g\to ss}$ are defined in the analogous way. $N$ is the number of total cells. As organisms undergo synchronous cell division, we have $N=2^n$ cells after the $n$ th cell division.

The composition effect profile $F_{\text{comp}}(x)$ captures how the cell division time depends on the proportion of soma-role cells $x=s/(s+g)$ present in an organism ($s$ and $g$ are the numbers of soma-role and germ-role cells). In this study, we use a functional form illustrated in Figure 1D and given by

(3) ${\displaystyle F_{\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{p}}(x)=\{\begin{array}{ll}1& \mathrm{f}\mathrm{o}\mathrm{r}0\le \mathrm{x}\le {\mathrm{x}}_0\\ 1-b+b\left(\frac{x_1-x}{x_1-x_0}\right)& \mathrm{f}\mathrm{o}\mathrm{r}{\mathrm{x}}_0<\mathrm{x}<{\mathrm{x}}_1\\ 1-b& \mathrm{f}\mathrm{o}\mathrm{r}{\mathrm{x}}_1\le \mathrm{x}\le 1\end{array}}$

With the functional form (3), soma-role cells can benefit to the organism growth, only if their proportion in the organism exceeds the contribution threshold x₀. Interactions between soma-role cells may lead to the synergistic (increase in the number of soma-role cells improves their efficiency), or discounting benefits (increase in the number of soma-role cells reduces their efficiency) to the organism growth, controlled by the contribution synergy parameter $\alpha$. The maximal achievable reduction in the cell division time is given by the maximal benefit $b$, realized beyond the saturation threshold x₁ of the soma-role cell proportion. A further increase in the proportion of soma-role cells does not provide any additional benefits. With the right combination of parameters, (3) is able to recover various characters of soma-role cells contribution to the organism growth: linear ($x_0=0,x_1=1,\alpha =1$), power-law ($x_0=0,x_1=1,\alpha \ne 1$), step-functions ($x_0=x_1$), and a huge range of other scenarios. Previous works have shown that convex (accelerating) performance functions favour cell differentiation (Michod, 2006; Rueffler et al., 2012; Cooper and West, 2018). The performance functions measure the performance of organisms with respect to different traits, such as fertility and viability. Lately, the form of functions favoring cell differentiation has been extended to be concave (decelerating) by including topological constraints in organisms (Yanni et al., 2020). Our model extends the form of performance functions by allowing it has a contribution threshold and saturation threshold.

Once the outcome of all cell divisions is known and the time needed to complete the current cell doubling round is computed, the current round ends and the next starts. The development completes after $n$ rounds. At this stage, the number of germ-role cells (organism offspring number) and the cumulative length of the life cycle are obtained.

In Gao et al., 2019, we have shown that the growth rate ($\lambda$) of a population, in which organisms undergo a stochastic development and fragmentation, is given by the solution of

(4) $\sum _i{G}_i{P}_i{e}^{-\lambda T_i}=1.$

Here, $i$ is the developmental trajectory – in our case, the specific combination of all cell division outcomes; $G_i$ is the number of offspring organisms produced at the end of developmental trajectory ${\displaystyle i}$, equal to the number of germ-role cells at the moment of maturity; $P_i$ is the probability that an organism development will follow the trajectory ${\displaystyle i}$; ${\displaystyle T_i}$ is the time necessary to complete the trajectory ${\displaystyle i}$ – from a single cell to the maturity size of $2^n$ cells.

For a given combination of differentiation costs ($c_{g\to s}$, $c_{s\to g}$) and a composition effect profile (determined by four parameters: x₀, x₁, $b$, and $\alpha$), we screen through a number of stochastic developmental strategies $D$ and identify the one providing the largest growth rate ($\lambda$) to the population. In this study, we searched for those parameters under which irreversible strategies lead to the fastest growth and are thus evolutionary optimal, see model details in Appendix A.

The vast majority of cells in a body of any multicellular being contains enough genetic information to build an entire new organism. However, in a typical metazoan species, very few cells actually participate in the organism reproduction – only a limited number of germ cells are capable of doing it. The other cells, called somatic cells, perform vegetative functions but do not contribute to reproduction – somatic differentiation is irreversible. We asked for the reason for the success of such a specific mode of organism development. We theoretically investigated the evolution of irreversible somatic differentiation with a model of clonally developing organisms taking into account benefits provided by soma-role cells, costs arising from cell differentiation, and the effect of the raw organism size.

Our key findings are:

• The evolution of irreversible somatic differentiation is inseparable from costly cell differentiation or risky cell differentiation.

• For irreversible somatic differentiation to evolve in organisms with synchronous cell division, somatic cells should be able to contribute to the organism performance already when their numbers are small.

• Only large enough organisms tend to develop irreversible somatic differentiation.

According to our results, cell differentiation costs are essential for the emergence of irreversible somatic differentiation, see Figure 2A. The costs punish strategies with high rate of cell differentiation. As a result, irreversible strategies gain an advantage because their overall differentiation rate is low, see Figure 2D, and soma-role cells do not differentiate at all. Most models focus on traits that lead to benefits for the organism, while the cost of cell differentiation are rarely considered. For cells in a multicellular organism, differentiation costs arise from the material needs, energy, and time it takes to produce components necessary for the performance of the differentiated cell, which were absent in the parent cell. For instance, in filamentous cyanobacteria nitrogen-fixating heterocysts develop much thicker cell wall than parent photosynthetic cells had. Also, reports indicate between 23% (Ow et al., 2008) and 74% (Sandh et al., 2014) of the proteome changes its abundance in heterocysts compared against photosynthetic cells. Similarly, the changes in the protein composition in the course of cell differentiation was found during the development of stalk and fruiting bodies of Dictyostelium discoideum (Bakthavatsalam and Gomer, 2010; Czarna et al., 2010).

An alternative to differentiation costs in terms of slower growth is a model with a risky differentiation, where we found similar patterns, see Figure 5. These results indicate that the exact mechanism of the differentiation costs does not play a major role in the evolution of irreversible somatic differentiation.

Our model demonstrates that irreversible somatic differentiation is more likely to evolve when a few soma-role cells are able to provide a substantial benefit to the organism, see Figure 3. Volvocales algae demonstrate that a significant contribution by small numbers of somatic cells might indeed be found in a natural population: In Eudorina illinoiensis, only four out of thirty-two cells are vegetative (Sambamurty AVSS, 2005) (soma-role in our terms). This species has developed some reproductive division of labour and a fraction of only $1/8$ of vegetative cells is sufficient for colony success. Thus, it seems possible that highly-efficient soma-role cells open the way to the evolution of irreversible somatic differentiation. Several patterns of how cells proved the benefit to an organism have been previously considered (Michod, 2007; Willensdorfer, 2009; Rossetti et al., 2010; Rueffler et al., 2012; Cooper and West, 2018; Yanni et al., 2020). The majority of papers focuses on the resource allocation toward different tasks in each cell in an organism and how divergent different cells can be. In our model, we assume that the germ-role and soma-role cell are different in function and focus on the relationship between the number of soma-role cells and their impact, e.g. the character of their interactions. While the found $F_{\text{comp}}$ curves exhibit convex-like shape, see Figure 3A,B, this finding has a different nature from the convex trade-off between fertility and viability found in the models of cell differentiation (Michod, 2007).

Our model shows that irreversible somatic differentiation does not evolve if the organism size is small, see Figure 4A. The maturity size plays an important role in an organism’s life cycle (Amado et al., 2018; Erten and Kokko, 2020): Large organisms have potential advantages to optimize themselves in multiple ways, such as to improve growth efficiency (Waters et al., 2010), to avoid predators (Matz and Kjelleberg, 2005; Fisher et al., 2016; Hiltunen and Becks, 2014), to increase problem-solving efficiency (Morand-Ferron and Quinn, 2011), and to exploit the division of labour in organisms (Carroll, 2001; Matt and Umen, 2016). Moreover, the maximum size has been related to the reproduction of the organism from the onset of multicellularity in Earth’s history (Ratcliff et al., 2012). Our results suggest that the smallest organism able to evolve irreversible somatic differentiation should typically be about 32–64 cells (unless the cost of soma-to-germ differentiation is extremely large and the cost of the reverse is low). This is in line with the pattern of development observed in Volvocales green algae. In Volvocales, cells are unable to move (vegetative function) and divide (reproductive function) simultaneously, as a unique set of centrioles are involved in both tasks (Wynne and Bold, 1985; Koufopanou, 1994). Chlamydomonas reinhardtii (unicellular) and Gonium pectorale (small colonies up to 16 cells) perform these tasks at different times. They move towards the top layers of water during the day to get more sunlight. At night, however, these species perform cell division and/or colony reproduction, slowly sinking down in the process. However, among larger Volvocales, a division of labour begins to develop. In Eudorina elegans colonies, containing 16–32 cells, a few cells at the pole have their chances to give rise to an offspring colony reduced (Marchant, 1977; Hallmann, 2011). In P. californica, half of the 128-celled colony is formed of smaller cells, which are totally dedicated to the colony movement and die at the end of colony life cycle (Kikuchi, 1978; Hallmann, 2011). In Volvox carteri, most of a 10,000 cell colony is formed by somatic cells, which die upon the release of offspring groups (Hallmann, 2011).

In a majority of our tests, we used the maturity size of 2¹⁰ = 1024 cells. This is significantly larger than the minimal necessary size for evolution of irreversible somatic differentiation. However, the body size of the order of 1000 cell attracts attention because at this scale organisms of very diverse degrees of complexity are observed: from undifferentiated colonies (ocean algae Phaeocystis antarctica), to intermediary life forms (slime molds slugs), to paradigm multicellular organisms (higher Volvocales and nematode Caenorhabditis elegans).

The model presented in our study focuses on the transition from colonial life forms to multicellular beings. Further development of complexity opens multiple new ways for optimization of life cycle. For example, a maternal organism can provide protection and nurture for offspring at their early stages of growth, like in V. carteri (10,000 cells) in which offspring colonies develop inside the parental organism. There, the rate of offspring growth depends mostly on the performance of the maternal organism and much less on the differentiation strategy of offspring. Having maternal protection allows to relax the conditions for evolution of irreversible differentiation indicated in our study. How much these conditions can be relaxed is a very interesting question.

One of the most significant assumptions we took is the synchronicity of cell divisions even if division outcomes are different. This is only possible if cell actions are coordinated at the level of organism – otherwise, cells that do not differentiate may complete their divisions before differentiating cells. When in the history of multicellularity such a coordination emerges is an open question. However, in a number of rather simple species, a synchronicity of cell divisions paired with cell differentiation is observed. One example is the green algae Eudorina illinoiensis – one of the simplest species demonstrating the first signs of reproductive division of labour, in which four out of 32 cells are differentiated (Sambamurty AVSS, 2005). Another example is 128-celled algae Pleodorina californica, half of the cells are differentiated. And still, the cell divisions are synchronous (Kikuchi, 1978). Even the size of the mature organism being a power of two indicates that cells do not divide independently, but their actions are controlled at the level of the organism.

To peek at the impact of the cell division synchronicity, we developed a model with asynchronous cell division, where cell differentiation costs are paid individually by each differentiating cell, see Appendix. G. We found that the evolution of irreversible differentiation is significantly suppressed even under the most favourable conditions ($c_{s\to g}\gg c_{g\to s}$) – the frequency of composition profiles promoting irreversible somatic differentiation is much smaller and the maturity size restriction is higher.

Another assumption, which shapes the results of our study, is the static differentiation strategy the probability of each division outcome does not depend on the stage of life cycle. On the one hand, the static nature of differentiation strategy puts irreversible differentiation in disadvantage, as it creates a trade-off between the fraction of soma-role cells at the early stage of life cycle and the number of germ-role cells at the end of life cycle. On the other hand, a set of fully flexible dynamic differentiation strategies present an efficient but hardly realistic solution to the life cycle optimization problem: at the first round of cell divisions organism converts to all-soma state and remains so until the last round, when all cells convert back to germ-state. Theoretically, this strategy provides simultaneously the fastest possible development rate (100% soma-role cells during life cycle) and the largest possible number of offspring (100% germ-role cells at the end of life cycle). Still, we cannot provide an example of such a developmental program in nature. Nevertheless, the differentiation strategy of higher Volvocales is not static Kirk, 2005 and the exploration of a vast space of dynamic differentiation strategies warrants further investigation.

We acknowledge that our discussion of natural examples of germ-soma differentiation relies heavily on Volvocales algae. This merely reflects the bias in the empirical literature about evolution of germ/soma differentiation towards this group. We should note that our model is not a model of Volvocales life cycle. Instead, we aim to answer the question about emergence of irreversible somatic differentiation in a broad context without tailoring it to the features of a single group.

Our study originated from curiosity about driving factors in the evolution of irreversible somatic differentiation: Why does the green algae Volvox from the kingdom Plantae shed most of its biomass in a single act of reproduction? And why, in another kingdom, Animalia, in most of the species the majority of body cells is outright forbidden to contribute to the next generation? Our results show which factors makes a difference between the evolution of an irreversible somatic differentiation and other strategies of development. One of these factors, the maturity size, is known in the context of the evolution of reproductive division of labour (Kirk, 2005). Another factor, the costs of cell differentiation, is, in general, discussed in a greater biological scope but is hardly acknowledged as a factor contributing to the evolution of organism development. Finally, the early contribution of soma-role cells to the organism growth, even if they are small in numbers, is an unexpected outcome of our investigation, overlooked so far as well. Despite the simplistic nature of our model (we did not aim to model any specific organism), all our results find a confirmation among the Volvocales clade. Hence, we expect that the findings of this study reveal general properties of the evolution of irreversible somatic differentiation, independently of the clade where it evolves.

The code implementing our model is deposited at https://github.com/YuanxiaoGao/Evolution-of-irreversible-somatic-differentiation (copy archived at https://archive.softwareheritage.org/swh:1:rev:9a1ea7c84f3041ebe3720e7837b28182912b5e00).

1. 1. Aktipis CA
   2. Boddy AM
   3. Jansen G
   4. Hibner U
   5. Hochberg ME
   6. Maley CC
   7. Wilkinson GS

   (2015) Cancer across the tree of life: cooperation and cheating in multicellularity

   Philosophical Transactions of the Royal Society B: Biological Sciences 370:20140219.

   https://doi.org/10.1098/rstb.2014.0219

   • PubMed
   • Google Scholar
2. 1. Amado A
   2. Batista C
   3. Campos PRA

   (2018) A mechanistic model for the evolution of multicellularity

   Physica A: Statistical Mechanics and Its Applications 492:1543–1554.

   https://doi.org/10.1016/j.physa.2017.11.080

   • Google Scholar
3. 1. Bakthavatsalam D
   2. Gomer RH

   (2010) The secreted proteome profile of developing Dictyostelium discoideum cells

   Proteomics 10:2556–2559.

   https://doi.org/10.1002/pmic.200900516

   • PubMed
   • Google Scholar
4. 1. Carroll SB

   (2001) Chance and necessity: the evolution of morphological complexity and diversity

   Nature 409:1102–1109.

   https://doi.org/10.1038/35059227

   • PubMed
   • Google Scholar
5. 1. Cooper GA
   2. West SA

   (2018) Division of labour and the evolution of extreme specialization

   Nature Ecology & Evolution 2:1161–1167.

   https://doi.org/10.1038/s41559-018-0564-9

   • PubMed
   • Google Scholar
6. 1. Czarna M
   2. Mathy G
   3. Mac'Cord A
   4. Dobson R
   5. Jarmuszkiewicz W
   6. Sluse-Goffart CM
   7. Leprince P
   8. De Pauw E
   9. Sluse FE

   (2010) Dynamics of the Dictyostelium discoideum mitochondrial proteome during vegetative growth, starvation and early stages of development

   Proteomics 10:6–22.

   https://doi.org/10.1002/pmic.200900352

   • PubMed
   • Google Scholar
7. 1. DuBuc TQ
   2. Schnitzler CE
   3. Chrysostomou E
   4. McMahon ET
   5. Febrimarsa
   6. Gahan JM
   7. Buggie T
   8. Gornik SG
   9. Hanley S
   10. Barreira SN
   11. Gonzalez P
   12. Baxevanis AD
   13. Frank U

   (2020) Transcription factor AP2 controls cnidarian germ cell induction

   Science 367:757–762.

   https://doi.org/10.1126/science.aay6782

   • PubMed
   • Google Scholar
8. 1. Erten EY
   2. Kokko H

   (2020) From zygote to a multicellular soma: Body size affects optimal growth strategies under cancer risk

   Evolutionary Applications 13:1593–1604.

   https://doi.org/10.1111/eva.12969

   • Google Scholar
9. 1. Fisher RM
   2. Bell T
   3. West SA

   (2016) Multicellular group formation in response to predators in the alga Chlorella vulgaris

   Journal of Evolutionary Biology 29:551–559.

   https://doi.org/10.1111/jeb.12804

   • Google Scholar
10. 1. Gallon JR

    (1992) Reconciling the incompatible: N2 fixation and O2

    The New Phytologist 129:571–609.

    https://doi.org/10.1111/J.1469-8137.1992.TB00087.X

    • Google Scholar
11. 1. Gao Y
    2. Traulsen A
    3. Pichugin Y

    (2019) Interacting cells driving the evolution of multicellular life cycles

    PLOS Computational Biology 15:e1006987.

    https://doi.org/10.1371/journal.pcbi.1006987

    • PubMed
    • Google Scholar
12. 1. Gavrilets S

    (2010) Rapid transition towards the division of labor via evolution of developmental plasticity

    PLOS Computational Biology 6:e1000805.

    https://doi.org/10.1371/journal.pcbi.1000805

    • PubMed
    • Google Scholar
13. 1. Goldsby HJ
    2. Dornhaus A
    3. Kerr B
    4. Ofria C

    (2012) Task-switching costs promote the evolution of division of labor and shifts in individuality

    PNAS 109:13686–13691.

    https://doi.org/10.1073/pnas.1202233109

    • PubMed
    • Google Scholar
14. 1. Goldsby HJ
    2. Knoester DB
    3. Kerr B
    4. Ofria C

    (2014) The effect of conflicting pressures on the evolution of division of labor

    PLOS ONE 9:e102713.

    https://doi.org/10.1371/journal.pone.0102713

    • PubMed
    • Google Scholar
15. 1. Grosberg RK
    2. Strathmann RR

    (1998) One cell, two cell, red cell, blue cell: the persistence of a unicellular stage in multicellular life histories

    Trends in Ecology & Evolution 13:112–116.

    https://doi.org/10.1016/S0169-5347(97)01313-X

    • PubMed
    • Google Scholar
16. 1. Grosberg RK
    2. Strathmann RR

    (2007) The Evolution of Multicellularity: A Minor Major Transition?

    Annual Review of Ecology, Evolution, and Systematics 38:621–654.

    https://doi.org/10.1146/annurev.ecolsys.36.102403.114735

    • Google Scholar
17. 1. Hallmann A

    (2011) Evolution of reproductive development in the volvocine algae

    Sexual Plant Reproduction 24:97–112.

    https://doi.org/10.1007/s00497-010-0158-4

    • PubMed
    • Google Scholar
18. 1. Hiltunen T
    2. Becks L

    (2014) Consumer co-evolution as an important component of the eco-evolutionary feedback

    Nature Communications 5:5226.

    https://doi.org/10.1038/ncomms6226

    • PubMed
    • Google Scholar
19. 1. Ispolatov I
    2. Ackermann M
    3. Doebeli M

    (2012) Division of labour and the evolution of multicellularity

    Proceedings of the Royal Society B: Biological Sciences 279:1768–1776.

    https://doi.org/10.1098/rspb.2011.1999

    • Google Scholar
20. 1. Kikuchi K

    (1978) Cellular differentiation in Pleodorina californica

    Cytologia 43:153–160.

    https://doi.org/10.1508/cytologia.43.153

    • Google Scholar
21. 1. Kirk DL

    (2005) A twelve-step program for evolving multicellularity and a division of labor

    BioEssays 27:299–310.

    https://doi.org/10.1002/bies.20197

    • PubMed
    • Google Scholar
22. 1. Koufopanou V

    (1994) The evolution of soma in the volvocales

    The American Naturalist 143:907–931.

    https://doi.org/10.1086/285639

    • Google Scholar
23. 1. Lanfear R

    (2018) Do plants have a segregated germline?

    PLOS Biology 16:e2005439.

    https://doi.org/10.1371/journal.pbio.2005439

    • PubMed
    • Google Scholar
24. 1. Marchant HJ

    (1977) Colony formation and inversion in the green alga Eudorina elegans

    Protoplasma 93:325–339.

    https://doi.org/10.1007/BF01275663

    • Google Scholar
25. 1. Matt G
    2. Umen J

    (2016) Volvox: a simple algal model for embryogenesis, morphogenesis and cellular differentiation

    Developmental Biology 419:99–113.

    https://doi.org/10.1016/j.ydbio.2016.07.014

    • PubMed
    • Google Scholar
26. 1. Matz C
    2. Kjelleberg S

    (2005) Off the hook--how Bacteria survive protozoan grazing

    Trends in Microbiology 13:302–307.

    https://doi.org/10.1016/j.tim.2005.05.009

    • PubMed
    • Google Scholar
27. 1. Michod RE

    (2006) The group covariance effect and fitness trade-offs during evolutionary transitions in individuality

    PNAS 103:9113–9117.

    https://doi.org/10.1073/pnas.0601080103

    • PubMed
    • Google Scholar
28. 1. Michod RE

    (2007) Evolution of individuality during the transition from unicellular to multicellular life

    PNAS 104 Suppl 1:8613–8618.

    https://doi.org/10.1073/pnas.0701489104

    • PubMed
    • Google Scholar
29. 1. Mikhailov KV
    2. Konstantinova AV
    3. Nikitin MA
    4. Troshin PV
    5. Rusin LY
    6. Lyubetsky VA
    7. Panchin YV
    8. Mylnikov AP
    9. Moroz LL
    10. Kumar S
    11. Aleoshin VV

    (2009) The origin of metazoa: a transition from temporal to spatial cell differentiation

    BioEssays 31:758–768.

    https://doi.org/10.1002/bies.200800214

    • PubMed
    • Google Scholar
30. 1. Morand-Ferron J
    2. Quinn JL

    (2011) Larger groups of passerines are more efficient problem solvers in the wild

    PNAS 108:15898–15903.

    https://doi.org/10.1073/pnas.1111560108

    • PubMed
    • Google Scholar
31. 1. Ow SY
    2. Cardona T
    3. Taton A
    4. Magnuson A
    5. Lindblad P
    6. Stensjö K
    7. Wright PC

    (2008) Quantitative shotgun proteomics of enriched heterocysts from Nostoc sp. PCC 7120 using 8-plex isobaric peptide tags

    Journal of Proteome Research 7:1615–1628.

    https://doi.org/10.1021/pr700604v

    • PubMed
    • Google Scholar
32. 1. Pichugin Y
    2. Peña J
    3. Rainey PB
    4. Traulsen A

    (2017) Fragmentation modes and the evolution of life cycles

    PLOS Computational Biology 13:e1005860.

    https://doi.org/10.1371/journal.pcbi.1005860

    • PubMed
    • Google Scholar
33. 1. Ratcliff WC
    2. Denison RF
    3. Borrello M
    4. Travisano M

    (2012) Experimental evolution of multicellularity

    PNAS 109:1595–1600.

    https://doi.org/10.1073/pnas.1115323109

    • PubMed
    • Google Scholar
34. 1. Rodrigues JFM
    2. Rankin DJ
    3. Rossetti V
    4. Wagner A
    5. Bagheri HC

    (2012) Differences in cell division rates drive the evolution of terminal differentiation in microbes

    PLOS Computational Biology 8:e1002468.

    https://doi.org/10.1371/journal.pcbi.1002468

    • PubMed
    • Google Scholar
35. 1. Rossetti V
    2. Schirrmeister BE
    3. Bernasconi MV
    4. Bagheri HC

    (2010) The evolutionary path to terminal differentiation and division of labor in cyanobacteria

    Journal of Theoretical Biology 262:23–34.

    https://doi.org/10.1016/j.jtbi.2009.09.009

    • PubMed
    • Google Scholar
36. 1. Rueffler C
    2. Hermisson J
    3. Wagner GP

    (2012) Evolution of functional specialization and division of labor

    PNAS 109:E326–E335.

    https://doi.org/10.1073/pnas.1110521109

    • PubMed
    • Google Scholar
37. Book

    1. Sambamurty AVSS

    (2005)

    A Textbook of Algae

    IK International Pvt Limited.

    • Google Scholar
38. 1. Sandh G
    2. Ramström M
    3. Stensjö K

    (2014) Analysis of the early heterocyst Cys-proteome in the multicellular Cyanobacterium Nostoc punctiforme reveals novel insights into the division of labor within diazotrophic filaments

    BMC Genomics 15:1064.

    https://doi.org/10.1186/1471-2164-15-1064

    • PubMed
    • Google Scholar
39. 1. Shelton DE
    2. Desnitskiy AG
    3. Michod RE

    (2012)

    Distributions of reproductive and somatic cell numbers in diverse Volvox (Chlorophyta) species

    Evolutionary Ecology Research 14:707.

    • PubMed
    • Google Scholar
40. 1. Solari CA
    2. Kessler JO
    3. Goldstein RE

    (2013) A general allometric and life-history model for cellular differentiation in the transition to multicellularity

    The American Naturalist 181:369–380.

    https://doi.org/10.1086/669151

    • PubMed
    • Google Scholar
41. 1. Tverskoi D
    2. Makarenkov V
    3. Aleskerov F

    (2018) Modeling functional specialization of a cell colony under different fecundity and viability rates and resource constraint

    PLOS ONE 13:e0201446.

    https://doi.org/10.1371/journal.pone.0201446

    • PubMed
    • Google Scholar
42. 1. Waters JS
    2. Holbrook CT
    3. Fewell JH
    4. Harrison JF

    (2010) Allometric scaling of metabolism, growth, and activity in whole colonies of the seed-harvester ant Pogonomyrmex californicus

    The American Naturalist 176:501–510.

    https://doi.org/10.1086/656266

    • PubMed
    • Google Scholar
43. 1. Willensdorfer M

    (2009) On the evolution of differentiated multicellularity

    Evolution 63:306–323.

    https://doi.org/10.1111/j.1558-5646.2008.00541.x

    • Google Scholar
44. Book

    1. Wynne MJ
    2. Bold HC

    (1985)

    Introduction to the Algae: Structure and Reproduction

    Prentice-Hall, Incorporated.

    • Google Scholar
45. 1. Yanni D
    2. Jacobeen S
    3. Márquez-Zacarías P
    4. Weitz JS
    5. Ratcliff WC
    6. Yunker PJ

    (2020) Topological constraints in early multicellularity favor reproductive division of labor

    eLife 9:e54348.

    https://doi.org/10.7554/eLife.54348

    • PubMed
    • Google Scholar

Author details

1. Yuanxiao Gao

   Max Planck Institute for Evolutionary Biology, Plön, Germany

   Contribution

   Conceptualization, Software, Formal analysis, Investigation, Visualization

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6719-8299

2. Hye Jin Park

   1. Max Planck Institute for Evolutionary Biology, Plön, Germany
   2. Asia Pacific Center for Theoretical Physics, Pohang, Republic of Korea
   3. Department of Physics, POSTECH, Pohang, Republic of Korea

   Contribution

   Conceptualization, Software, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3552-6275

3. Arne Traulsen

   Max Planck Institute for Evolutionary Biology, Plön, Germany

   Contribution

   Conceptualization, Supervision

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0669-5267

4. Yuriy Pichugin

   Max Planck Institute for Evolutionary Biology, Plön, Germany

   Present address

   Department of Ecology and Evolutionary Biology, Princeton University, Princeton, United States

   Contribution

   Conceptualization, Methodology, Project administration

   For correspondence

   pichugin@evolbio.mpg.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3078-2499

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