Program in Ecology, Evolution, and Conservation Biology, University of Illinois at Urbana–Champaign, Urbana, United States
Vikyath D Rao
Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana–Champaign, Urbana, United States; Department of Physics, University of Illinois at Urbana–Champaign, Urbana, United States
Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana–Champaign, Urbana, United States; Swarm Intelligence and Complex Systems Group, Department of Computer Science, Leipzig University, Leipzig, Germany
Tobias Jagla
Swarm Intelligence and Complex Systems Group, Department of Computer Science, Leipzig University, Leipzig, Germany
Amy C Cash-Ahmed
Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana–Champaign, Urbana, United States
Swarm Intelligence and Complex Systems Group, Department of Computer Science, Leipzig University, Leipzig, Germany
Saurabh Sinha
Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana–Champaign, Urbana, United States; Center for Biophysics and Quantitative Biology, University of Illinois at Urbana–Champaign, Urbana, United States
Department of Biomedical Engineering, University of Michigan, Ann Arbor, United States; Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, United States
Program in Ecology, Evolution, and Conservation Biology, University of Illinois at Urbana–Champaign, Urbana, United States; Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana–Champaign, Urbana, United States; Neuroscience Program, University of Illinois at Urbana–Champaign, Urbana, United States; Department of Entomology, University of Illinois at Urbana–Champaign, Urbana, United States
Understanding the regulatory architecture of phenotypic variation is a fundamental goal in biology, but connections between gene regulatory network (GRN) activity and individual differences in behavior are poorly understood. We characterized the molecular basis of behavioral plasticity in queenless honey bee (Apis mellifera) colonies, where individuals engage in both reproductive and non-reproductive behaviors. Using high-throughput behavioral tracking, we discovered these colonies contain a continuum of phenotypes, with some individuals specialized for either egg-laying or foraging and ‘generalists’ that perform both. Brain gene expression and chromatin accessibility profiles were correlated with behavioral variation, with generalists intermediate in behavior and molecular profiles. Models of brain GRNs constructed for individuals revealed that transcription factor (TF) activity was highly predictive of behavior, and behavior-associated regulatory regions had more TF motifs. These results provide new insights into the important role played by brain GRN plasticity in the regulation of behavior, with implications for social evolution.
