Uncovering the mesendoderm gene regulatory network through multi-omic data integration

• Published in: Cell reports (Cambridge) 2022-02, Vol.38 (7), p.110364-110364, Article 110364
• Main Authors: 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, Cho, Ken W.Y.
• Format: Article
• Language: English
• Subjects: Animals; ATAC-seq; ChIP-seq; Chromatin - metabolism; cis-regulatory modules; Consensus Sequence - genetics; DNA - metabolism; endoderm; Endoderm - embryology; Gastrulation - genetics; Gene Expression Regulation, Developmental; Gene Regulatory Networks; Genomics; linked self-organizing maps; mesoderm; Mesoderm - embryology; multi-omic; Protein Binding; RNA - metabolism; RNA-seq; Transcription Factors - metabolism; Transcription, Genetic; Xenopus; Xenopus - embryology; Xenopus - genetics
• ISSN: 2211-1247; 2211-1247
• DOI: 10.1016/j.celrep.2022.110364

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. [Display omitted] •Built a pipeline to create GRNs using highly dimensional multi-omic datasets•Predict new TF-DNA/TF-TF interactions during mesendoderm development•Generate the genome-scale vertebrate mesendodermal network with high fidelity•Develop an SOM metaclusters visualization resource for multiple RNA- and ChIP-seq sets Building mechanistic gene regulatory networks (GRNs) from highly dimensional genomic datasets to recapitulate cellular differentiation remains a difficult problem in developmental biology. Jansen et al. use linked self-organizing maps to combine ChIP-seq/ATAC-seq with temporal, spatial, and perturbation RNA-seq data from Xenopus tropicalis mesendoderm development to build a high-resolution genome-scale mechanistic GRN.