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Advancing the understanding of the embryo transcriptome co-regulation using meta-, functional, and gene network analysis tools

¹ Department of Animal Sciences, ² Institute for Genomic Biology, ³ Department of Computer Sciences and ⁴ Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA

Correspondence should be addressed to S L Rodriguez-Zas, University of Illinois at Urbana-Champaign, 1207 West Gregory Drive, Urbana, Illinois 61801, USA; Email: rodrgzzs{at}uiuc.edu

Embryo development is a complex process orchestrated by hundreds of genes and influenced by multiple environmental factors. We demonstrate the application of simple and effective meta-study and gene network analyses strategies to characterize the co-regulation of the embryo transcriptome in a systems biology framework. A meta-analysis of nine microarray experiments aimed at characterizing the effect of agents potentially harmful to mouse embryos improved the ability to accurately characterize gene co-expression patterns compared with traditional within-study approaches. Simple overlap of significant gene lists may result in under-identification of genes differentially expressed. Sample-level meta-analysis techniques are recommended when common treatment levels or samples are present in more than one study. Otherwise, study-level meta-analysis of standardized estimates provided information on the significance and direction of the differential expression. Cell communication pathways were highly represented among the genes differentially expressed across studies. Mixture and dependence Bayesian network approaches were able to reconstruct embryo-specific interactions among genes in the adherens junction, axon guidance, and actin cytoskeleton pathways. Gene networks inferred by both approaches were mostly consistent with minor differences due to the complementary nature of the methodologies. The top–down approach used to characterize gene networks can offer insights into the mechanisms by which the conditions studied influence gene expression. Our work illustrates that further examination of gene expression information from microarray studies including meta- and gene network analyses can help characterize transcript co-regulation and identify biomarkers for the reproductive and embryonic processes under a wide range of conditions.

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