NERC-FAPESP: Unravelling the evolutionary processes shaping greenbeard recognition systems and the control of cooperative behaviour

Project: Research council

Project Details

Description

Organisms often make self sacrifices that benefit members of their group. These may be complex behaviours, such as watching for predators while others forage, or relatively simple behaviours, such as bacteria that produce molecules that help others grow. Why do individuals make these costly sacrifices when they could simply avoid the costs and freeload on the sacrifices made by others? This question has perplexed biologists for decades. A potential solution comes from a (selfish) genetic perspective on evolution, which suggests that genes that result in costly helping behaviours can be favoured if the benefits are reaped by other individuals carrying copies of those genes (so the gene ends up helping copies of itself). Indeed, in many biological systems, individuals help relatives because they have an increased likelihood of sharing genes (e.g. siblings have a 50:50 chance of gene sharing). Obviously, an even better strategy would be for them to identify and direct benefits to others who definitely share copies of their genes. Richard Dawkins' captured this idea in a thought experiment where a single gene produces a signal (a green beard), identifies that signal in others, and modifies behaviour to direct help towards other green bearded individuals. Such 'greenbeard' systems would appear to provide the perfect solution. However, if a greenbeard gene arose it would provide such a big advantage that eventually all individuals would have the same greenbeard (and all individuals would help one another all the time). This is not seen in biological systems. Furthermore, biologists have argued that it is implausible for a single gene to encode all the different properties required (signal, recognition, helping behaviour). Contrary to these expectations, greenbeard genes have been described in a diverse array of organisms that still select who or when to help others. Clearly our understanding of the evolutionary processes that shape recognition, cooperation, and the role of greenbeard genes is insufficient. We will address this problem by combining mathematical theory with experimental tests in a fascinating microbial system.

Our solution to this problem comes from recognising the importance of two puzzling yet common features of greenbeards identified in nature. Firstly, different individuals of the same species typically have different greenbeard gene sequences, which causes them to have different properties. Secondly, greenbeards tend to be composed of several genes found next to each other in the genome. Our hypothesis is that these properties are required to allow different interacting individuals to measure whether these genes are shared and then use the amount of gene sharing to determine how much they are willing to make self-sacrifices that benefit their group. We have developed a mathematical framework that we will use to explore this hypothesis theoretically. We will also perform experimental studies to test this theory. For this, we will use a simple microbial model, Dictyostelium discoideum. This system is ideally suited because single-celled individuals come together in groups, where some cells sacrifice themselves and die to help the remaining cells disperse as spores. We have previously demonstrated that different strains measure their relatedness to their group and then adjust how much of a sacrifice they are willing to make (so they make less of a self-sacrifice when they are not with relatives). We have also demonstrated that these amoebae have a greenbeard composed of two genes, which differ in each strain. We will investigate the link between these greenbeard genes and the decision of how much to cooperate, how they have evolved, the type of variation they contain, and dissect the underlying mechanisms that allow these to encode all the implausible properties of a greenbeard.
StatusActive
Effective start/end date20/09/2119/09/24

Collaborative partners

Funding

  • Natural Environment Research Council

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