1. Uncovering the mesendoderm gene regulatory network through multi-omic data integration
- Author
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Jansen, Camden, Paraiso, Kitt D, Zhou, Jeff J, Blitz, Ira L, Fish, Margaret B, Charney, Rebekah M, Cho, Jin Sun, Yasuoka, Yuuri, Sudou, Norihiro, Bright, Ann Rose, Wlizla, Marcin, Veenstra, Gert Jan C, Taira, Masanori, Zorn, Aaron M, Mortazavi, Ali, and Cho, Ken WY
- Subjects
Biochemistry and Cell Biology ,Bioinformatics and Computational Biology ,Genetics ,Biological Sciences ,Human Genome ,Stem Cell Research ,1.1 Normal biological development and functioning ,Underpinning research ,Generic health relevance ,Animals ,Chromatin ,Consensus Sequence ,DNA ,Endoderm ,Gastrulation ,Gene Expression Regulation ,Developmental ,Gene Regulatory Networks ,Genomics ,Mesoderm ,Protein Binding ,RNA ,Transcription Factors ,Transcription ,Genetic ,Xenopus ,ATAC-seq ,ChIP-seq ,RNA-seq ,cis-regulatory modules ,endoderm ,gene regulatory networks ,linked self-organizing maps ,mesoderm ,multi-omic ,Medical Physiology ,Biological sciences - 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.
- Published
- 2022