Uncovering the mesendoderm gene regulatory network through multi-omic data integration
Ontology highlight
ABSTRACT:
ORGANISM(S): Xenopus tropicalis
PROVIDER: GSE161600 | GEO | 2022/03/30
REPOSITORIES: GEO
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Project description: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.
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Project description:Uncovering the mesendoderm gene regulatory network through multi-omic data integration
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Project description:BackgroundType 2 diabetes (T2D) onset is a complex, organized biological process with multilevel regulation, and its physiopathological mechanisms are yet to be elucidated. This study aims to find out the key drivers and pathways involved in the pathogenesis of T2D through multi-omics analysis.MethodsThe datasets used in the experiments comprise three groups: (1) genomic (2) transcriptomic, and (3) epigenomic categories. Then, a series of bioinformatics technologies including Marker set enrichment analysis (MSEA), weighted key driver analysis (wKDA) was performed to identify key drivers. The hub genes were further verified by the Receiver Operator Characteristic (ROC) Curve analysis, proteomic analysis, and Real-time quantitative polymerase chain reaction (RT-qPCR). The multi-omics network was applied to the Pharmomics pipeline in Mergeomics to identify drug candidates for T2D treatment. Then, we used the drug-gene interaction network to conduct network pharmacological analysis. Besides, molecular docking was performed using AutoDock/Vina, a computational docking program.ResultsModule-gene interaction network was constructed using MSEA, which revealed a significant enrichment of immune-related activities and glucose metabolism. Top 10 key drivers (PSMB9, COL1A1, COL4A1, HLA-DQB1, COL3A1, IRF7, COL5A1, CD74, HLA-DQA1, and HLA-DRB1) were selected by wKDA analysis. Among these, COL5A1, IRF7, CD74, and HLA-DRB1 were verified to have the capability to diagnose T2D, and expression levels of PSMB9 and CD74 had significantly higher in T2D patients. We further predict the co-expression network and transcription factor (TF) binding specificity of the key driver. Besides, based on module interaction networks and key driver networks, 17 compounds are considered to possess T2D-control potential, such as sunitinib.ConclusionsWe identified signature genes, biomolecular processes, and pathways using multi-omics networks. Moreover, our computational network analysis revealed potential novel strategies for pharmacologic interventions of T2D.
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Project description:TCF7L1 is a member of the T cell factor/Lymphoid enhancer factor (TCF/LEF) family of tran-scription factors that are part of the WNT/beta-CATENIN signaling pathway. TCF7L1 modulates transcription by interacting with other regulators on chromatin. TCF7L1 has been shown to be one of the key factors in maintaining pluripotency in human embryonic stem cells (ESCs). We previously demonstrated that the absence of TCF71 causes H9 hESCs to differentiate sponta-neously (Sierra et al., 2018 Development 145(4).pii:dev161075). Mechanistically, how TCF7L1 inhibits differentiation and keeps cells in the pluripotent state is not clear. Identifying transcrip-tional regulators on chromatin is a critical step to elucidating one of several molecular mecha-nisms controlled by TCF7L1. We previously developed a FLAG tagged TCF7L1 transgene that is controlled by a doxycycline-inducible TET-ON system and generated the H9-TCF7L1 line (Sier-ra et al., 2018 Development 145(4).pii:dev161075). Here we used the H9-TCF7L1 line and em-ployed the method called rapid immunoprecipitation (IP) mass spectrometry of endogenous pro-tein (RIME) (Mohammed et al., 2016 Nat Protoc 11(2):316-26) to identify TCF7L1-associated proteins on chromatin. Coupling of these two methods (the FLAG tagged TETON inducible sys-tem and RIME) allowed us to control the level of TCF7L1 expression in hESCs, immunopreci-pate TCF7L1 using an anti-FLAG antibody and capture TCF7L1-bound associated complexes on chromatin. Our MS analysis identified some known proteins that have been shown to associate with the WNT/beta-CATENIN/TCF/LEF pathway, as well as novel complexes that have not been linked with TCF7L1. Gene Ontology analysis suggest these proteins function in chromatin modi-fication, splicing, and RNA processing. Our data could create new ideas for in-depth studies of TCF7L1 controlling pluripotency in human ESCs and for understanding how TCF7L1 may act in other cell types.
2024-06-16 | PXD047582 | Pride
Project description:MotivationCells respond to environments by regulating gene expression to exploit resources optimally. Recent advances in technologies allow for measuring the abundances of RNA, proteins, lipids and metabolites. These highly complex datasets reflect the states of the different layers in a biological system. Multi-omics is the integration of these disparate methods and data to gain a clearer picture of the biological state. Multi-omic studies of the proteome and metabolome are becoming more common as mass spectrometry technology continues to be democratized. However, knowledge extraction through the integration of these data remains challenging.ResultsConnections between molecules in different omic layers were discovered through a combination of machine learning and model interpretation. Discovered connections reflected protein control (ProC) over metabolites. Proteins discovered to control citrate were mapped onto known genetic and metabolic networks, revealing that these protein regulators are novel. Further, clustering the magnitudes of ProC over all metabolites enabled the prediction of five gene functions, each of which was validated experimentally. Two uncharacterized genes, YJR120W and YDL157C, were accurately predicted to modulate mitochondrial translation. Functions for three incompletely characterized genes were also predicted and validated, including SDH9, ISC1 and FMP52. A website enables results exploration and also MIMaL analysis of user-supplied multi-omic data.Availability and implementationThe website for MIMaL is at https://mimal.app. Code for the website is at https://github.com/qdickinson/mimal-website. Code to implement MIMaL is at https://github.com/jessegmeyerlab/MIMaL.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:Genome-wide maps of TCF7L1 binding during early adipogenesis
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Project description:We defined the genome-wide binding regions of Smad2/3 and Foxh1 at mid-gastrula stage Xenopus tropicalis embryos, at which Nodal signaling and Foxh1 are critical in mesendoderm specification program.
2014-11-07 | GSE53652 | GEO
Project description:Epigenetic mechanisms set apart the active and inactive regions in the genome of multicellular organisms to produce distinct cell fates during embryogenesis. Here, we report on the epigenetic and transcriptome genome-wide maps of gastrula-stage Xenopus tropicalis embryos using massive parallel sequencing of cDNA (RNA-seq) and DNA obtained by chromatin immunoprecipitation (ChIP-seq) of histone H3 K4 and K27 trimethylation and RNA Polymerase II (RNAPII). These maps identify promoters and transcribed regions. Strikingly, genomic regions featuring opposing histone modifications are mostly transcribed, reflecting spatially regulated expression rather than bivalency as determined by expression profile analyses, sequential ChIP, and ChIP-seq on dissected embryos. Spatial differences in H3K27me3 deposition are predictive of localized gene expression. Moreover, the appearance of H3K4me3 coincides with zygotic gene activation, whereas H3K27me3 is predominantly deposited upon subsequent spatial restriction or repression of transcriptional regulators. These results reveal a hierarchy in the spatial control of zygotic gene activation. ChIP-seq profiles of two histone modifications (H3K4me3 and H3K27me3) and RNA Polymerase II, and a RNA-seq profile, of gastrula stage Xenopus tropicalis embryos
2009-09-15 | E-GEOD-14025 | biostudies-arrayexpress
Project description:Studying the complex relationship between transcription, translation and protein degradation is essential to our understanding of biological processes in health and disease. The limited correlations observed between mRNA and protein abundance suggest pervasive regulation of post-transcriptional steps and support the importance of profiling mRNA levels in parallel to protein synthesis and degradation rates. In this work, we applied an integrative multi-omic approach to study gene expression along the mammalian cell cycle through side-by-side analysis of mRNA, translation and protein levels. Our analysis sheds new light on the significant contribution of both protein synthesis and degradation to the variance in protein expression. Furthermore, we find that translation regulation plays an important role at S-phase, while progression through mitosis is predominantly controlled by changes in either mRNA levels or protein stability. Specific molecular functions are found to be co-regulated and share similar patterns of mRNA, translation and protein expression along the cell cycle. Notably, these include genes and entire pathways not previously implicated in cell cycle progression, demonstrating the potential of this approach to identify novel regulatory mechanisms beyond those revealed by traditional expression profiling. Through this three-level analysis, we characterize different mechanisms of gene expression, discover new cycling gene products and highlight the importance and utility of combining datasets generated using different techniques that monitor distinct steps of gene expression.
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Project description:Here we show through genome-wide binding studies that transcription factor 7-like 1 (TCF7L1) represses structure-related genes during adipogenesis. Intriguingly, TCF7L1 is induced in a cell contact-dependent manner by confluency in preadipocytes and is required for adipocyte differentiation by repressing transcription of cell structure genes. TCF7L1 is also sufficient to bestow adipogenic potential upon non-adipogenic cells. These results implicate TCF7L1 as a novel adipogenic competency factor that uniquely determines adipogenic fate through cell structure organization required for adipocyte gene activation. Examination of TCF7L1 binding in preadipocytes treated for 24 hours with adipogenic stimuli.
2011-09-02 | E-GEOD-31867 | biostudies-arrayexpress