Journal Article

DNA Methylation and Genome Evolution in Honeybee: Gene Length, Expression, Functional Enrichment Covary with the Evolutionary Signature of DNA Methylation

Jia Zeng and Soojin V. Yi

in Genome Biology and Evolution

Published on behalf of Society for Molecular Biology and Evolution

Volume 2, issue , pages 770-780
Published in print January 2010 |
Published online October 2010 | e-ISSN: 1759-6653 | DOI: http://dx.doi.org/10.1093/gbe/evq060

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A growing body of evidence suggests that DNA methylation is functionally divergent among different taxa. The recently discovered functional methylation system in the honeybee Apis mellifera presents an attractive invertebrate model system to study evolution and function of DNA methylation. In the honeybee, DNA methylation is mostly targeted toward transcription units (gene bodies) of a subset of genes. Here, we report an intriguing covariation of length and epigenetic status of honeybee genes. Hypermethylated and hypomethylated genes in honeybee are dramatically different in their lengths for both exons and introns. By analyzing orthologs in Drosophila melanogaster, Acyrthosiphon pisum, and Ciona intestinalis, we show genes that were short and long in the past are now preferentially situated in hyper- and hypomethylated classes respectively, in the honeybee. Moreover, we demonstrate that a subset of high-CpG genes are conspicuously longer than expected under the evolutionary relationship alone and that they are enriched in specific functional categories. We suggest that gene length evolution in the honeybee is partially driven by evolutionary forces related to regulation of gene expression, which in turn is associated with DNA methylation. However, lineage-specific patterns of gene length evolution suggest that there may exist additional forces underlying the observed interaction between DNA methylation and gene lengths in the honeybee.

Keywords: honeybee; DNA methylation; gene lengths; gene expression

Journal Article.  6262 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology ; Evolutionary Biology ; Genetics and Genomics

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