Genome-Wide Analysis of GLD-1–Mediated mRNA Regulation Suggests a Role in mRNA Storage

Translational repression is often accompanied by mRNA degradation. In contrast, many mRNAs in germ cells and neurons are “stored" in the cytoplasm in a repressed but stable form. Unlike repression, the stabilization of these mRNAs is surprisingly little understood. A key player in Caenorhabditis elegans germ cell development is the STAR domain protein GLD-1. By genome-wide analysis of mRNA regulation in the germ line, we observed that GLD-1 has a widespread role in repressing translation but, importantly, also in stabilizing a sub-population of its mRNA targets. Additionally, these mRNAs appear to be stabilized by the DDX6-like RNA helicase CGH-1, which is a conserved component of germ granules and processing bodies. Because many GLD-1 and CGH-1 stabilized mRNAs encode factors important for the oocyte-to-embryo transition (OET), our findings suggest that the regulation by GLD-1 and CGH-1 serves two purposes. Firstly, GLD-1–dependent repression prevents precocious translation of OET–promoting mRNAs. Secondly, GLD-1– and CGH-1–dependent stabilization ensures that these mRNAs are sufficiently abundant for robust translation when activated during OET. In the absence of this protective mechanism, the accumulation of OET–promoting mRNAs, and consequently the oocyte-to-embryo transition, might be compromised

Computational solutions for omics data

High-throughput experimental technologies are generating increasingly massive and complex genomic data sets. The sheer enormity and heterogeneity of these data threaten to make the arising problems computationally infeasible. Fortunately, powerful algorithmic techniques lead to software that can answer important biomedical questions in practice. In this Review, we sample the algorithmic landscape, focusing on state-of-the-art techniques, the understanding of which will aid the bench biologist in analysing omics data. We spotlight specific examples that have facilitated and enriched analyses of sequence, transcriptomic and network data sets.National Institutes of Health (U.S.) (Grant GM081871