Location: Honey Bee ResearchTitle: New meta-analysis tools reveal common transciptional regulatory basis for multiple determinants of behavior) Author
|Uribe-rubio, Jose Luis|
Submitted to: Proceedings of the National Academy of Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/23/2012
Publication Date: N/A
Citation: Interpretive Summary: Honey bees progress through a series of complex behaviors as adults. Some are prompted by nutritional factors while others are under hormonal control. The age when a worker begins to forage is a major maturational milestone in bee life, and is determined by a variety of genetic and environmental factors that induce differential expression of thousands of genes in the brain. This manuscript describes new statistical tools for identifying significant relationships between the expression patterns of single or combinations of genetic elements underlying behavior. The result of these statistical tools suggests that many different factors that affect behavior use the same elements for regulating gene expression. Though these elements exert common effects, they might be employed in distinct ways depending upon the factor and resulting behavior.
Technical Abstract: : A fundamental problem in meta-analysis is how to systematically combine information from multiple statistical tests to rigorously evaluate a single overarching hypothesis. This occurs in systems biology when attempting to map genomic attributes to complex phenotypes such as behavior. Behavior and other complex phenotypes are influenced by intrinsic and environmental determinants that act on the transcriptome, but little is known about how these determinants interact at the molecular level. We developed an informatic technique that for the first time identifies statistically significant meta- associations between gene expression patterns and transcription factor combinations. Deploying this technique for brain transcriptome profiles from ca. 400 individual bees, we show that diverse determinants of behavior rely on specific, shared combinations of transcription factors. This regulatory code would have been missed by traditional gene co-expression or cis-regulatory analytic methods. We expect that our new meta-analysis tools will be useful for a broad array of problems in systems biology and other fields.