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

• Grant number: 20/10018-8
• Support Opportunities: Regular Research Grants
• Start date: October 01, 2021
• End date: September 30, 2025
• Field of knowledge: Biological Sciences - Genetics
• Agreement: NERC, UKRI
• Principal Investigator: Diogo Meyer
• Grantee: Diogo Meyer
• Principal researcher abroad: Christopher Thompson
• Institution abroad: University College London (UCL), England
• Principal researcher abroad: Jason B Wolf
• Institution abroad: University of Bath, England
• Host Institution: Instituto de Biociências (IB). Universidade de São Paulo (USP). São Paulo , SP, Brazil

Abstract

Organisms often make self-sacrifices that help groupmates. Despite the costs, such actions are ultimately worthwhile because individuals benefit other copies of their own genes. From this genetic perspective, a selfish gene would do best if it could measure its relatedness to the group and adjust the level of cooperation in proportion to the share of the benefits that go to other copies of itself. To be such a strategist, a gene must signal its presence within an individual, identify that signal in others, and respond appropriately by modulating cooperative behaviour. These are the defining properties of 'greenbeard' genes. While several greenbeard genes have been identified, we still know very little about how they work, why they persist, and why they typically share several features, such as being multigenic and highly polymorphic (i.e., 'polychromatic'). To understand greenbeard genes, we must decipher their signal-receiver properties, identify the mechanisms that translate signal information into behavioural responses, and uncover the processes that govern their evolution. To achieve these goals, we will study the greenbeard Tgr locus and the role it plays in governing facultative cooperation in the social amoeba Dictyostelium. Specifically, we will integrate mathematical population genetic models with experimental and computational approaches to characterise: 1) patterns of protein variation and evolution, 2) the impact of this protein variation on the signal-receiver properties, 3) the pleiotropic costs and benefits associated with this variation, and 4) the molecular mechanisms that tie Tgr signalling to the cell fate responses that represent the social output from the system. Together, our proposed workplan will provide broad insights into how these gatekeepers of social interactions operate and evolve. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

BITARELLO, BARBARA D.; BRANDT, DEBORA Y. C.; MEYER, DIOGO; ANDRES, AIDA M.. Inferring Balancing Selection From Genome-Scale Data. GENOME BIOLOGY AND EVOLUTION, v. 15, n. 3, p. 18-pg., 2023-03-03. (20/10018-8)