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Beilharz, T. H.

Publications and source records attributed to Beilharz, T. H..

2 recordsLinked to original sources

Widespread cytoplasmic polyadenylation programs asymmetry in the germline and early embryo

BACKGROUNDThe program of embryonic development is launched by selective activation of a silent maternal transcriptome. In Caenorhabditis elegans, nuclei of the adult germline are responsible for the synthesis of at least two distinct mRNA populations; those required for housekeeping functions, and those that program the oocyte-to-embryo transition. We mapped this separation by changes to the length-distribution of poly(A)-tails that depend on GLD-2 mediated cytoplasmic polyadenylation and its regulators genome-wide.\n\nRESULTSMore than 1000 targets of cytoplasmic polyadenylation were identified by differential polyadenylation. Amongst mRNA with the greatest dependence on GLD-2 were those encoding RNA binding proteins with known roles in spatiotemporal patterning such as mex-5 and pos-1. In General, the 3 UTR of GLD-2 targets were longer, contained cytosine-patches, and were enriched for non-standard polyadenylation-motifs. To identify the deadenylase that initiated transcript silencing, we depleted the known deadenylases in the gld-2(0) mutant background. Only the loss of CCF-1 suppressed the short-tailed phenotype of GLD-2 targets suggesting that in addition to its general role in RNA turnover, this is the major deadenylase for regulatory silencing of maternal mRNA. Analysis of poly(A)-tail length-change in the embryo lacking specific RNA-binding proteins revealed new candidates for asymmetric expression in the first embryonic divisions.\n\nCONCLUSIONThe concerted action of RNA binding proteins exquisitely regulates GLD-2 activity in space and time. We present our data as interactive web resources for a model where GLD-2 mediated cytoplasmic polyadenylation regulates target mRNA at each stage of worm germline and early embryonic development.

developmental biology

Topconfects: a package for confident effect sizes in differential expression analysis provides improved usability ranking genes of interest

BackgroundA differential gene expression analysis may produce a set of significantly differentially expressed genes that is too large to easily investigate, so that a means of ranking genes by their biological interest level is desirable. The life-sciences have grappled with the abuse of p-values to rank genes for this purpose. As an alternative, a lower confidence bound on the magnitude of Log Fold Change (LFC) could be used to rank genes, but it has been unclear how to reconcile this with the need to perform False Discovery Rate (FDR) correction. The TREAT test of McCarthy and Smyth is a step in this direction, finding genes significantly exceeding a specified LFC threshold. Here we describe the use of test inversion on TREAT to present genes ranked by a confidence bound on the LFC, while still controlling FDR.\n\nResultsTesting the Topconfects R package with simulated gene expression data shows the method outperforming current statistical approaches across a wide range of experiment sizes in the identification of genes with largest LFCs. Applying the method to a TCGA breast cancer data-set shows the method ranks some genes with large LFC higher than would traditional ranking by p-value. Importantly these two ranking methods lead to a different biological emphasis, in terms both of specific highly ranked genes and gene-set enrichment.\n\nConclusionsThe choice of ranking method in differential expression analysis can affect the biological interpretation. The common default of ranking by p-value is implicitly by an effect size in which each gene is standardized to its own variability, rather than comparing genes on a common scale, which may not be appropriate. The Topconfects approach of presenting genes ranked by confident LFC effect size is a variation on the TREAT method with improved usability, removing the need to fine-tune a threshold parameter and removing the temptation to abuse p-values as a de-facto effect size.

bioinformatics