UC Merced

Models of gene expression during development traditionally focus on the regulation of mRNA transcription. However, an essential level of control occurs via mRNA decay. mRNA decay is likely to be important during nervous system development, where the structure of neurons requires localized translation of mRNAs far from their site of synthesis and the generation of cellular diversity requires rapid turnover of mRNAs that regulate proliferation and differentiation. The study of mRNA decay during embryonic development has previously been hindered by the lack of methods allowing in vivo, cell type-specific measurements of transcript stability. We have developed a technique, called TU-decay that overcomes this technical challenge and allows neural-specific, genome-wide measurements of mRNA decay in intact Drosophila embryos. This technique provides the foundation for a systems-level approach that we are using to construct a neural development mRNA decay network. Our comparisons of whole embryo and neural-specific mRNA half-lives have identified mRNAs that are selectively stabilized or destabilized in the nervous system. TU-decay analysis has also revealed transcript decay kinetics that correlate with the function of the encoded protein. For example, mRNAs that are known to be translated within axon growth cones or dendrites have long half-lives while mRNAs encoding signaling proteins and transcription factors that regulate cell fate decisions have short half-lives. AU-rich element (ARE) containing transcripts were analyzed to investigate the role of known cis-regulatory elements in determining neural mRNA stability. Examples of both low and high stability ARE-containing mRNAs were identified in this analysis. Also, this analysis provided evidence that other mRNA sequence features, including micro-RNA binding sites and alternative polyadenylation, may have combinatorial effects in determining the stability of ARE-containing mRNAs. This work lays the foundation for future analyses aimed at generating a comprehensive and predictive network map of neural mRNA decay dynamics, thus filling a significant gap in current models of gene expression during neural development.