Identifying a developmental transition in honey bees using gene expression data

PLOS Computational Biology · 9 authors, 6 centres

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TL;DR

The study developed a statistical method to detect a bistable gene expression pattern marking the behavioral transition from nest bees to foragers in honey bees.

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The researchers developed a Bayesian method to identify a transition to bistability from high-dimensional gene expression data, using the expected shape of a distribution near a continuous transition. They applied this method to 91 genes measured in 16 honey bees at five developmental timepoints from 1 to 15 days old. The method linked the emergence of bistability to specific genes known to be involved in the behavioral shift from nest work to foraging. This proof-of-concept demonstrates a general approach for identifying collective transitions from gene expression data by inferring distribution shapes, going beyond simple correlations.

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other

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biomedical research

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ORIGINAL PAPER

PMC10547183

Identifying a developmental transition in honey bees using gene expression data

PLOS Computational Biology

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ABSTRACT

In many organisms, interactions among genes lead to multiple functional states, and changes to interactions can lead to transitions into new states. These transitions can be related to bifurcations (or critical points) in dynamical systems theory. Characterizing these collective transitions is a major challenge for systems biology. Here, we develop a statistical method for identifying bistability near a continuous transition directly from high-dimensional gene expression data. We apply the method to data from honey bees, where a known developmental transition occurs between bees performing tasks in the nest and leaving the nest to forage. Our method, which makes use of the expected shape of the distribution of gene expression levels near a transition, successfully identifies the emergence of bistability and links it to genes that are known to be involved in the behavioral transition. This proof of concept demonstrates that going beyond correlative analysis to infer the shape of gene expression distributions might be used more generally to identify collective transitions from gene expression data.

CITATION EXPORT

Bryan C. Daniels, Ying Wang, Robert E. Page et al.. Identifying a developmental transition in honey bees using gene expression data. PLOS Computational Biology. (2023). https://doi.org/10.1371/journal.pcbi.1010704

AUTHORS

9 · 6 CENTRES

Bryan C. Daniels, Ying Wang, Robert E. Page, Gro V. Amdam, Kiran R. Patil, Carl Herrmann, Kiran R. Patil, Mark Alber, Mark Alber

1 School of Complex Adaptive Systems, Arizona State University, Tempe, Arizona, United States of America
2 Banner Health Corporation, Phoenix, Arizona, United States of America
3 School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
4 Department of Entomology and Nematology, University of California Davis, Davis, California, United States of America
5 Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, Aas, Norway
University of California Riverside, UNITED STATES

EDITOR / REVIEWER

Mark Alber

Bryan C. Daniels, Ying Wang, Robert E. Page et al.. Identifying a developmental transition in honey bees using gene expression data. PLOS Computational Biology. (2023). https://doi.org/10.1371/journal.pcbi.1010704