Research Article: Interacting cells driving the evolution of multicellular life cycles

Date Published: May 14, 2019

Publisher: Public Library of Science

Author(s): Yuanxiao Gao, Arne Traulsen, Yuriy Pichugin, Natalia L. Komarova

Abstract: Evolution of complex multicellular life began from the emergence of a life cycle involving the formation of cell clusters. The opportunity for cells to interact within clusters provided them with an advantage over unicellular life forms. However, what kind of interactions may lead to the evolution of multicellular life cycles? Here, we combine evolutionary game theory with a model for the emergence of multicellular groups to investigate how cell interactions can influence reproduction modes during the early stages of the evolution of multicellularity. In our model, the presence of both cell types is maintained by stochastic phenotype switching during cell division. We identify evolutionary optimal life cycles as those which maximize the population growth rate. Among all interactions captured by two-player games, the vast majority promotes two classes of life cycles: (i) splitting into unicellular propagules or (ii) fragmentation into two offspring clusters of equal (or almost equal) size. Our findings indicate that the three most important characteristics, determining whether multicellular life cycles will evolve, are the average performance of homogeneous groups, heterogeneous groups, and solitary cells.

Partial Text: The evolution of multicellular life cycles is one of the most challenging questions of modern evolutionary biology. In the history of life, multicellular organisms have independently originated at least 25 times from unicellular ancestors [1]. From the very beginning, multicellular life has been shaped by interactions between different cells within heterogeneous groups [2, 3]. The role of these interactions in the emergence (or prevention) of multicellularity is an open question. Recently, there has been a rising interest in the evolution of life cycles including multicellular stages from both experimentalists [4–8] and theoreticians [9–15]. Multicellular clusters can emerge either as the result of clonal development (staying together in terms of [10]) or by aggregation of cells and smaller clusters (coming together). In the present study, we focus on competition between various “staying together” life cycles. The life cycle that leads to the fastest population growth would eventually dominate the population. We address how interactions between different cells within heterogeneous groups affect the growth competition between unicellular and multicellular life cycles. When interactions between different types of individuals within one group accelerate growth, more complex forms of multicellularity are expected to evolve in the long run.

We consider a group-structured population, where individuals of two phenotypes A and B are nested into groups. These groups incrementally grow by one cell at a time and fragment into smaller offspring groups upon reaching a critical size of M cells. For a given group, the time between cell divisions depends only on the size of this group and its cell composition. Thus, the growth of the group is independent of other groups and therefore at the level of groups, the population growth is density independent. Therefore, in the long run, the population converges to a stationary regime, characterised by exponential growth at a rate we call λ. As populations employing different life cycles (different critical size and/or fragmentation mode) have different growth rates, the life cycle with the largest growth rate λ will eventually take over the population.

Our model allows us to calculate the growth rate of any given life cycle provided the elements of the payoff matrix (a, b, c, d), the phenotype switching probability m, and the profile of the size-dependent component of development time (Ti+j). Here, we focus on life cycles having the largest λ, as these will be the winners of evolutionary growth competition.

In our study we performed an extensive investigation of the competition of life cycles driven by interactions between cells within in a group. Key to this study is the consideration of all possible reproduction modes and all possible interactions captured by game theoretic 2 × 2 payoff matrices. Among the huge variety of reproduction modes, only eight were found to be evolutionarily optimal, see Figs 2 and 4. Moreover, the vast majority games promotes either of two very specific classes of life cycles: fragmentation into strictly unicellular offspring (1+…+1) or production of exactly two strictly multicellular daughter groups of identical (or almost identical) size. Intuitively, life cycles with unicellular offspring should be promoted when the cells grow fast in a homogeneous group, as the single cell bottleneck eliminates heterogeneity in the most effective way. Similarly, when the cells grow fast in a heterogeneous group, life cycles with multicellular offspring should be promoted as they are best at preserving heterogeneity. Our results, in general, support this intuition. However, the current work reveals a much broader picture and we observed a number of less intuitive features of life cycle evolution driven by cell interactions. First, we observed the transition between these two major life cycles classes. This occurs via transitional life cycles mixing unicellular and multicellular offspring (such as 2+1 and 2+1+1), see Fig 4C. Second, we found that if being in a heterogeneous groups incurs a moderate penalty onto the cell, growth competition may still promote the life cycle with only multicellular offspring (2+2), even at high rates of phenotype switching (m), see Fig 4A. Third, an arbitrary strong penalty to heterogeneous groups (ϕ < 0), may still lead to the evolution of life cycles with multicellular offspring (2+2) given small enough m, see Fig 4A and 4C. Altogether, even with only eight life cycles observed, our model exhibits a rich behaviour and gives insights into factors shaping the evolution of life cycles. Source: http://doi.org/10.1371/journal.pcbi.1006987