Dataset Information

Functional genomic analysis of the let-7 regulatory network in Caenorhabditis elegans.

ABSTRACT: The let-7 microRNA (miRNA) regulates cellular differentiation across many animal species. Loss of let-7 activity causes abnormal development in Caenorhabditis elegans and unchecked cellular proliferation in human cells, which contributes to tumorigenesis. These defects are due to improper expression of protein-coding genes normally under let-7 regulation. While some direct targets of let-7 have been identified, the genome-wide effect of let-7 insufficiency in a developing animal has not been fully investigated. Here we report the results of molecular and genetic assays aimed at determining the global network of genes regulated by let-7 in C. elegans. By screening for mis-regulated genes that also contribute to let-7 mutant phenotypes, we derived a list of physiologically relevant potential targets of let-7 regulation. Twenty new suppressors of the rupturing vulva or extra seam cell division phenotypes characteristic of let-7 mutants emerged. Three of these genes, opt-2, prmt-1, and T27D12.1, were found to associate with Argonaute in a let-7-dependent manner and are likely novel direct targets of this miRNA. Overall, a complex network of genes with various activities is subject to let-7 regulation to coordinate developmental timing across tissues during worm development.

SUBMITTER: Hunter SE

PROVIDER: S-EPMC3597506 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Publications

Functional genomic analysis of the let-7 regulatory network in Caenorhabditis elegans.

Hunter Shaun E SE Finnegan Emily F EF Zisoulis Dimitrios G DG Lovci Michael T MT Melnik-Martinez Katya V KV Yeo Gene W GW Pasquinelli Amy E AE

PLoS genetics 20130314 3

The let-7 microRNA (miRNA) regulates cellular differentiation across many animal species. Loss of let-7 activity causes abnormal development in Caenorhabditis elegans and unchecked cellular proliferation in human cells, which contributes to tumorigenesis. These defects are due to improper expression of protein-coding genes normally under let-7 regulation. While some direct targets of let-7 have been identified, the genome-wide effect of let-7 insufficiency in a developing animal has not been ful ...[more]

PMID: 23516374

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Project description:BackgroundComplex gene regulatory networks underlie many cellular and developmental processes. While a variety of experimental approaches can be used to discover how genes interact, few biological systems have been systematically evaluated to the extent required for an experimental definition of the underlying network. Therefore, the development of computational methods that can use limited experimental data to define and model a gene regulatory network would provide a useful tool to evaluate many important but incompletely understood biological processes. Such methods can assist in extracting all relevant information from data that are available, identify unexpected regulatory relationships and prioritize future experiments.ResultsTo facilitate the analysis of gene regulatory networks, we have developed a computational modeling pipeline method that complements traditional evaluation of experimental data. For a proof-of-concept example, we have focused on the gene regulatory network in the nematode C. elegans that mediates the developmental choice between mesodermal (muscle) and ectodermal (skin) cell fates in the embryonic C lineage. We have used gene expression data to build two models: a knowledge-driven model based on gene expression changes following gene perturbation experiments, and a data-driven mathematical model derived from time-course gene expression data recovered from wild-type animals. We show that both models can identify a rich set of network gene interactions. Importantly, the mathematical model built only from wild-type data can predict interactions demonstrated by the perturbation experiments better than chance, and better than an existing knowledge-driven model built from the same data set. The mathematical model also provides new biological insight, including a dissection of zygotic from maternal functions of a key transcriptional regulator, PAL-1, and identification of non-redundant activities of the T-box genes tbx-8 and tbx-9.ConclusionsThis work provides a strong example for a mathematical modeling approach that solely uses wild-type data to predict an underlying gene regulatory network. The modeling approach complements traditional methods of data analysis, suggesting non-intuitive network relationships and guiding future experiments.

Dataset Information

Functional genomic analysis of the let-7 regulatory network in Caenorhabditis elegans.

Publications

Functional genomic analysis of the let-7 regulatory network in Caenorhabditis elegans.

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