201. Uncovering the mesendoderm gene regulatory network through multi-omic data integration.
- Author
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Jansen C, Paraiso KD, Zhou JJ, Blitz IL, Fish MB, Charney RM, Cho JS, Yasuoka Y, Sudou N, Bright AR, Wlizla M, Veenstra GJC, Taira M, Zorn AM, Mortazavi A, and Cho KWY
- Subjects
- Animals, Chromatin metabolism, Consensus Sequence genetics, DNA metabolism, Gastrulation genetics, Gene Expression Regulation, Developmental, Protein Binding, RNA metabolism, Transcription Factors metabolism, Transcription, Genetic, Endoderm embryology, Gene Regulatory Networks, Genomics, Mesoderm embryology, Xenopus embryology, Xenopus genetics
- Abstract
Mesendodermal specification is one of the earliest events in embryogenesis, where cells first acquire distinct identities. Cell differentiation is a highly regulated process that involves the function of numerous transcription factors (TFs) and signaling molecules, which can be described with gene regulatory networks (GRNs). Cell differentiation GRNs are difficult to build because existing mechanistic methods are low throughput, and high-throughput methods tend to be non-mechanistic. Additionally, integrating highly dimensional data composed of more than two data types is challenging. Here, we use linked self-organizing maps to combine chromatin immunoprecipitation sequencing (ChIP-seq)/ATAC-seq with temporal, spatial, and perturbation RNA sequencing (RNA-seq) data from Xenopus tropicalis mesendoderm development to build a high-resolution genome scale mechanistic GRN. We recover both known and previously unsuspected TF-DNA/TF-TF interactions validated through reporter assays. Our analysis provides insights into transcriptional regulation of early cell fate decisions and provides a general approach to building GRNs using highly dimensional multi-omic datasets., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)
- Published
- 2022
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