Social norms—describing the genetic basis

Figure: The study included three bees and three wasps representing four independent social origins (circles; non-social sibling species not shown) and a range of social and environmental phenotypes . The images shown are the bee species Ceratina calcarata (above) and the wasp species Polistes dominula (below).
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Credit: Drawings by Katherine S. Geist. Photos by Sandra Rehan and Seirian Sumner.

Beginning with Darwin, biologists have always been fascinated by social change. In its extreme form, eusocial species show a division of labor where certain individuals perform reproductive tasks such as egg laying, while others play non-reproductive roles such as foraging, building nest, and protection. This type of system requires individuals to sacrifice their own reproductive success to help reproduce others in their group, an idea that at first appears to be inconsistent with basic evolutionary principles ( that is, natural choice in individuals). Although the bee may be the most well-known example of multiple social species, the complex organization of the bee represents only one aspect of the many social structures that can be seen among the Hymenoptera. , which includes bees, wasps and ants. On the other hand, there are unusual social structures that involve, at the basic level, the cooperation of only a few people and their children. While most research to date on insect interactions has focused on complex social patterns, understanding the evolution of these rare species may help reveal early changes in social behavior. The authors of a new study published in Genome Biology and Evolution, entitled “Co-expression gene networks and machine-learning algorithms develop a basic genetic tool for the division of reproductive function in exotic insect populations,” which aims to fill this gap. According to first author Emeline Favreau, “Our work was unique in that we focused on six species of bees and wasps that are not social elites, but have unusual behavior patterns. cooperation, and as close relatives of many forms of life.” By using machine learning algorithms to analyze gene expression in six species representing multiple social origins, the authors discovered a shared genetic toolkit for friendship, which may form the basis for the transformation of complex social systems.

The international team of researchers included Katherine S. Geist (first co-author) and Amy L. Toth from Iowa State University, Christopher DR Wyatt and Seirian Sumner from the University of London, and Sandra M. Rehan of York University in Toronto. The authors worked together on this topic “because we all find it important to understand the origins of friendship,” says Favreau. “We were in the field watching the amazing diversity of community life, such as large busy wasp nests or small carpenter bees arranging their young in the small branches of trees. We always wondered: But how did this behavior come about? In this paper, we delve deep into evolutionary history to uncover molecular evidence for the emergence of social organization. ”

The study involved a comparative analysis of data from three bee species and three wasp species representing four independent social origins: the halictid bee. Megalopta genalisxylocopine bees Ceratina australensis and C. calcaratastenogastrine wasp Liostenogaster flavolinataand the polystine wasp Polistes canadensis and Q. the lady. Favreau explains: “When we used global genetic information in the brains of different behavioral groups (reproductive and asexual females), we found that there is a core set of genes associated with these basic social divisions in bees and wasps. This is exciting because it suggests that there may be common molecular ‘heads’ associated with the cooperation and all kinds of living things.”

Several functional groups found to be associated with sociality in this study are also associated with sociality in other social bees and ants. These include genes related to chromatin binding, DNA binding, telomere length regulation, and reproduction and metabolism. On the other hand, the study also found many genes and functional groups related to social phenotypes. According to the authors, these findings “reveal how taxon-specific molecular patterns complement the basic tool of molecular patterns in the sculpting of traits associated with sexual evolution.”

Interestingly, Favreau notes that “the machine learning approach to these large databases was a great way to discover these similarities.” Although the authors tried the first methods to study the different types of genes, these species are divided by phylogeny and failed to identify groups of genes related to the population. On the other hand, machine learning tools have provided a “non-invasive and sensitive approach,” allowing the authors to identify genetic similarities across a wide evolutionary space.

One question that remains is whether the findings of this study, which focused on species with unusual social patterns, can be compared to independent species with morphologically different breeding and breeding populations. childless. According to Favreau, “This is something we are working on right now and we hope to be able to solve it soon. We are taking a broad approach to examine whether genes and genetics are how it changes during population change.” This includes entering data for 16 other bee and wasp species, enabling “a larger comparative study of independent wasp and bee species, with and unusual friendship, and complicated friendship.”

Expanding the study however requires obtaining samples from around the world, a task that has sometimes proved difficult. It was really a challenge to find many of these species, some of which had never been genetically researched!” Favreau notes. “Due to the diversity of taxa around the world and the remote locations where they were collected, we are happy to have been able to find all the specimens and genes due to the global pandemic and travel restrictions over the years” a few years ago.” The team was eventually able to obtain several samples by collaborating with other researchers and organizations, underscoring the important role of collaboration in scientific discovery.

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