Individual differences in honey bee behavior enabled by plasticity in brain gene regulatory networks
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...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2020-12-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/62850 |
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doaj-74860396f58243798e5aeebaecdde6f8 |
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Article |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Beryl M Jones Vikyath D Rao Tim Gernat Tobias Jagla Amy C Cash-Ahmed Benjamin ER Rubin Troy J Comi Shounak Bhogale Syed S Husain Charles Blatti Martin Middendorf Saurabh Sinha Sriram Chandrasekaran Gene E Robinson |
| spellingShingle |
Beryl M Jones Vikyath D Rao Tim Gernat Tobias Jagla Amy C Cash-Ahmed Benjamin ER Rubin Troy J Comi Shounak Bhogale Syed S Husain Charles Blatti Martin Middendorf Saurabh Sinha Sriram Chandrasekaran Gene E Robinson Individual differences in honey bee behavior enabled by plasticity in brain gene regulatory networks eLife Apis mellifera behavioral plasticity gene regulation |
| author_facet |
Beryl M Jones Vikyath D Rao Tim Gernat Tobias Jagla Amy C Cash-Ahmed Benjamin ER Rubin Troy J Comi Shounak Bhogale Syed S Husain Charles Blatti Martin Middendorf Saurabh Sinha Sriram Chandrasekaran Gene E Robinson |
| author_sort |
Beryl M Jones |
| title |
Individual differences in honey bee behavior enabled by plasticity in brain gene regulatory networks |
| title_short |
Individual differences in honey bee behavior enabled by plasticity in brain gene regulatory networks |
| title_full |
Individual differences in honey bee behavior enabled by plasticity in brain gene regulatory networks |
| title_fullStr |
Individual differences in honey bee behavior enabled by plasticity in brain gene regulatory networks |
| title_full_unstemmed |
Individual differences in honey bee behavior enabled by plasticity in brain gene regulatory networks |
| title_sort |
individual differences in honey bee behavior enabled by plasticity in brain gene regulatory networks |
| publisher |
eLife Sciences Publications Ltd |
| series |
eLife |
| issn |
2050-084X |
| publishDate |
2020-12-01 |
| description |
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. |
| topic |
Apis mellifera behavioral plasticity gene regulation |
| url |
https://elifesciences.org/articles/62850 |
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doaj-74860396f58243798e5aeebaecdde6f82021-05-05T21:53:05ZengeLife Sciences Publications LtdeLife2050-084X2020-12-01910.7554/eLife.62850Individual differences in honey bee behavior enabled by plasticity in brain gene regulatory networksBeryl M Jones0https://orcid.org/0000-0003-2925-0807Vikyath D Rao1Tim Gernat2https://orcid.org/0000-0002-5977-3900Tobias Jagla3Amy C Cash-Ahmed4Benjamin ER Rubin5https://orcid.org/0000-0002-6766-0439Troy J Comi6https://orcid.org/0000-0002-3215-4026Shounak Bhogale7Syed S Husain8Charles Blatti9Martin Middendorf10https://orcid.org/0000-0002-5426-1092Saurabh Sinha11Sriram Chandrasekaran12https://orcid.org/0000-0002-8405-5708Gene E Robinson13https://orcid.org/0000-0003-4828-4068Program in Ecology, Evolution, and Conservation Biology, University of Illinois at Urbana–Champaign, Urbana, United StatesCarl 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 StatesCarl 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, GermanySwarm Intelligence and Complex Systems Group, Department of Computer Science, Leipzig University, Leipzig, GermanyCarl R. Woese Institute for Genomic Biology, University of Illinois at Urbana–Champaign, Urbana, United StatesLewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, United StatesLewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, United StatesCenter for Biophysics and Quantitative Biology, University of Illinois at Urbana–Champaign, Urbana, United StatesDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, United StatesCarl R. Woese Institute for Genomic Biology, University of Illinois at Urbana–Champaign, Urbana, United StatesSwarm Intelligence and Complex Systems Group, Department of Computer Science, Leipzig University, Leipzig, GermanyCarl 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 StatesDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, United States; Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, United StatesProgram 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 StatesUnderstanding 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.https://elifesciences.org/articles/62850Apis melliferabehavioral plasticitygene regulation |