Functional genomics: molecular basis underlying complex behaviors and evolution in bees

Abstract

Bees comprise a highly speciose group. It has been recognized 20,452 species around the World. Their role as pollinators of native and commercial plants is unquestionable benefiting human beings and the environment by their ecosystem services free of charge. Bees have been studied for centuries because aside their ecological features they present the intriguing behavior of eusociality. The large amount of species and behavior specificities lead to proportional lack of general biology knowledge. Moreover, the vast majority of studies have been restricted to few a species, as Apis mellifera and from genus Bombus. The advent of new techniques, as Next Generation Sequencing, has enabling to generate genomic data for non-model species and encouraging the investigation of old and new intriguing questions, such as evolution od eusociality, caste, presence of more than one queen in a nest (poliginy versus monoginy), host-parasite relationship, diapause, among many others. Recently, our research group has been contributed toward the understanding of social behavior evolution and diapause by using genomic data and non-model species. Here we present a grant proposal aiming at continuing the genomic investigation of complex behavior, now focusing on cleptoparasitism and cleptobiosis (subproject 1), monogonic and poliginic colonies existence (subproject 2), and reproductive diapause (subproject 3). The biological models are mostly native Brazilian bees, or occur predominantly in Brazilian territory, as Lestrimelitta limao (highly eusocial and cleptobiotic parasite of other eusocial species), Melipona bicolor (displays colonies mono and poliginics), and Plebeia remota (presents reproductive diapause). The models for the cleptoparasitism behavior are Tetrapedia diversipes and Coelioxiodes waltheriae, species of American distribution and Megachile e Coelioxys, cosmopolitan species. The main objective is to understand complex behavior and their evolution by using genomic data and thus identify gene networks and genetic signatures underlying such behaviors. This project involves several undergraduate and graduate students and a collaborator researcher, a former graduate student from our lab, currently as PD at the Université Libre de Buxelles, Belgium. (AU)