The social amoeba Dictyostelium discoideum is an important model system for the study of cooperation and multicellularity as is has both unicellular and aggregative life phases. In the aggregative phase, which typically occurs when nutrients are limiting, individual cells eventually gather together to form a fruiting bodies whose spores may be dispersed to another, better, location and whose stalk cells, which support the spores, die. This extreme form of cooperation has been the focus of numerous studies that have revealed the importance genetic relatedness and kin selection (Hamilton 1964; Lehmann and Rousset 2014) in explaining the maintenance of this cooperative collective behavior (Strassmann et al. 2000; Kuzdzal-Fick et al. 2011; Strassmann and Queller 2011). However, much remains unknown with respect to how the interactions between individual cells, their neighbors, and their environment produce cooperative behavior at the scale of whole groups or collectives. In this preprint, Forget et al. (2021) describe how the D. discoideum system is crucial in this respect because it allows these cellular-level interactions to be studied in a systematic and tractable manner.
Spore bias, which is the tendency of a particular genotype or strain to disproportionately migrate to the spore instead of the stalk, is often used to define which strains are "cheaters" (positive spore bias) and which are "cooperative" (negative spore bias). Forget et al. (2021) note that spore bias depends on a number of stochastic factors including external drivers such as variation in environmental (or nutrient) quality and internal drivers like cell-cycle phase at the time of starvation. Spore bias is also affected by the social environment where the fraction of cheater strains in a spore may be limited by the ability of the remaining stalk cells to support the spore. The social environment can also affect cells through their differential responsiveness to the chemical factors that induce differentiation into stalk cells; responsiveness is partly a function of nutrient quality (Thompson and Kay 2000), which in turn can be a function of cell density. Thus, Forget et al. (2021) highlight a number of mechanisms that could generate frequency-dependent selection that would lead to the stable maintenance of multiple strains with different spore biases; in other words, both cheater and cooperative strains might stably coexist due to these cellular-level interactions.
The cellular-level interactions that Forget et al. (2021) highlight are particularly important because they pose a challenge evolutionary theory: some evolutionary models of social and collective behavior neglect or simplify these interactions. For example, Forget et al. (2021) note that the developmental, behavior, and environmental timescales relevant for Dictyostelium fruiting body formation all overlap. Evolutionary analyses often assume some of these timescales, for example developmental and behavior, are separate in order to simplify the analysis of any interactions. Thus, new theoretical work that allows these timescales to overlap may shed light on how cellular-level interactions can produce environmental, physiological, and behavioral feedbacks that drive the evolution of cooperation and other collective behaviors.
Forget, M., Adiba, S. and De Monte, S.(2021) Social conflicts in *Dictyostelium discoideum *: a matter of scales. HAL, hal-03088868, ver. 2 peer-reviewed and recommended by PCI Evolutionary Biology. https://hal.archives-ouvertes.fr/hal-03088868/
Hamilton, W. D. (1964). The genetical evolution of social behaviour. II. Journal of theoretical biology, 7(1), 17-52. doi: https://doi.org/10.1016/0022-5193(64)90039-6
Kuzdzal-Fick, J. J., Fox, S. A., Strassmann, J. E., and Queller, D. C. (2011). High relatedness is necessary and sufficient to maintain multicellularity in Dictyostelium. Science, 334(6062), 1548-1551. doi: https://doi.org/10.1126/science.1213272
Lehmann, L., and Rousset, F. (2014). The genetical theory of social behaviour. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1642), 20130357. doi: https://doi.org/10.1098/rstb.2013.0357
Strassmann, J. E., and Queller, D. C. (2011). Evolution of cooperation and control of cheating in a social microbe. Proceedings of the National Academy of Sciences, 108(Supplement 2), 10855-10862. doi: https://doi.org/10.1073/pnas.1102451108
Strassmann, J. E., Zhu, Y., & Queller, D. C. (2000). Altruism and social cheating in the social amoeba Dictyostelium discoideum. Nature, 408(6815), 965-967. doi: https://doi.org/10.1038/35050087
Thompson, C. R., & Kay, R. R. (2000). Cell-fate choice in Dictyostelium: intrinsic biases modulate sensitivity to DIF signaling. Developmental biology, 227(1), 56-64. doi: https://doi.org/10.1006/dbio.2000.9877
DOI or URL of the preprint: 10.20944/preprints202008.0554.v1
Social conflicts in Dictyostelium discoideum : a matter of scales
In this manuscript, the authors describe two different conceptual perspectives for understanding the evolution of aggregation and collective behavior in the social amoeba Dictyostelium discoideum. The first perspective is the "strain-level" where empirically strain frequencies are used to determine the success of cooperative (less spore bias) or non-cooperative (more spore bias) strategies. The second perspective is the "cell-level" where individual cell fates, spore or stalk, are affected both local biotic and abiotic conditions and by stochastic forces. The paper has a lot of excellent detail about how aggregation and cooperation in Dictyostelium functions and might be evolutionarily stable. The "cell-level" perspective highlights a number of important mechanisms that contribute to spore or stalk bias including the cell-cell signaling, cell position, and cell-cycle stage. These mechanisms suggest there is important and understudied complexity in the experimental results of chimeric mixtures and suggest evolutionary models must account for these mechanistic details in order to truly describe how aggregation evolves and is maintained in Dictyostelium lineages.
Two reviewers have read the manuscript and agree that preprint is interesting and provides a valuable perspective. They provides a few important areas for improvement that I think the authors should consider. One area that I would like to highlight specifically is the reviewer's comments about the "the balance of arguments in favor of the strain- level vs. the cell-level perspective". I agree here with the reviewer that manuscript reads a bit more as an exposition on the importance of the cell-level perspective and less of a full comparison of the benefits and drawbacks of both approaches. I also agree that simply signaling this goal earlier in the paper would be a good way to address this issue.
One place that I think the manuscript needs more substantial modification is in its description of the mathematical theory in relationship to the strain vs cell-level perspective. On page 9 in section 3, the authors suggest pure strategy models are sufficient for the strain-level perspective and mixed strategy models are necessary for the cell-level perspective. In actuality, the cell-level perspective doesn't necessitate the use of mixed strategies any more than the strain-level does. A pure strategy can be deterministic or probabilistic. In fact, many models of cooperation use a continuous variable to measure the level of cooperation, which conceptually is no different than if that same variable measures a continuous probability of cooperating. Mixed strategies become relevant when one considers the possibility of a mixture of discrete pure strategies. But in many cases this is indistinguishable from a continuous of pure strategies where the strategy is a probability. The second issue on page 9 is that the paragraph suggests that simply by using a mixed strategy, certain models allow coexistence of different behaviors (stalk vs spore I assume). However, these models really show an equilibrium with both behaviors because they setup a game that is no longer a simple prisoner's dilemma (PD); rather, these games are likely snowdrift (SD) games where a mixed strategy or intermediate value is stable. In other words, its the change of the game structure in these models, not their consideration of mixed strategies per se, that leads to coexistence. This applies to n-players games too where some n-players games result in PD like games and others have nonlinearities that lead to SD like outcomes, but being an n-player game per se doesn't result in nonlinearities (e.g., "Such games naturally introduce frequency-dependent payoffs and non-linearities" on page 10). In a few other places the authors suggest that multiplayer games add additional complexity that requires new game theoretic approaches rather than traditional deterministic approaches (the paragraph on page 21 starting "In evolutionary game theory"). This also isn't true (see for example Peña et al 2014 J Theor Biol and Peña and Nöldeke 2015 J Theor Biol). In general, the authors should take more care about connecting any specific feature of the strain or cell perspective to a technical limitation of a specific game theoretic approach or model; its much more likely that specific models made specific biological assumptions such as regarding the payoff structure of the game the organisms play that resulted in the model's predictions rather than a specific technical aspect of the model analysis.
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