scholarly journals A gene regulatory network for neural induction

2021 ◽  
Author(s):  
Katherine E. Trevers ◽  
Hui-Chun Lu ◽  
Youwen Yang ◽  
Alexandre Thiery ◽  
Anna C. Strobl ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Time Course ◽  
Temporal Dynamics ◽  
Neural Induction ◽  
Neural Plate ◽  
Vertebrate Development ◽  
Initial Exposure ◽  
Reporter Assays ◽  
Gene Regulatory

During early vertebrate development, signals from a special region of the embryo, the organizer, can re-direct the fate of non-neural ectoderm cells to form a complete, patterned nervous system. This is called neural induction and has generally been imagined as a single signaling event, causing a switch of fate. Here we undertake a comprehensive analysis, in very fine time-course, of the events following exposure of ectoderm to the organizer. Using transcriptomics and epigenomics we generate a Gene Regulatory Network comprising 175 transcriptional regulators and 5,614 predicted interactions between them, with fine temporal dynamics from initial exposure to the signals to expression of mature neural plate markers. Using in situ hybridization, single-cell RNA-sequencing and reporter assays we show that neural induction by a grafted organizer mimics normal neural plate development. The study is accompanied by a comprehensive resource including information about conservation of the predicted enhancers in different vertebrate systems.

scholarly journals A gene regulatory network that underlies the derivation of the anterior neural plate from the epiblast

2010 ◽  
Vol 344 (1) ◽  
pp. 495
Author(s):  
Makiko Iwafuchi-Doi ◽  
Tatsuya Takemoto ◽  
Yuzo Yoshida ◽  
Isao Matsuo ◽  
Jun Aruga ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Neural Plate ◽  
Gene Regulatory ◽  
Anterior Neural Plate

scholarly journals Intracellular and Intercellular Gene Regulatory Network Inference From Time-Course Individual RNA-Seq

2021 ◽  
Vol 1 ◽  
Author(s):  
Makoto Kashima ◽  
Yuki Shida ◽  
Takashi Yamashiro ◽  
Hiromi Hirata ◽  
Hiroshi Kurosaka
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Time Course ◽  
Network Inference ◽  
Molecular Mechanisms ◽  
Rna Seq ◽  
Gene Regulatory Network Inference ◽  
Gene Regulatory ◽  
Multicellular Organisms

Gene regulatory network (GRN) inference is an effective approach to understand the molecular mechanisms underlying biological events. Generally, GRN inference mainly targets intracellular regulatory relationships such as transcription factors and their associated targets. In multicellular organisms, there are both intracellular and intercellular regulatory mechanisms. Thus, we hypothesize that GRNs inferred from time-course individual (whole embryo) RNA-Seq during development can reveal intercellular regulatory relationships (signaling pathways) underlying the development. Here, we conducted time-course bulk RNA-Seq of individual mouse embryos during early development, followed by pseudo-time analysis and GRN inference. The results demonstrated that GRN inference from RNA-Seq with pseudo-time can be applied for individual bulk RNA-Seq similar to scRNA-Seq. Validation using an experimental-source-based database showed that our approach could significantly infer GRN for all transcription factors in the database. Furthermore, the inferred ligand-related and receptor-related downstream genes were significantly overlapped. Thus, the inferred GRN based on whole organism could include intercellular regulatory relationships, which cannot be inferred from scRNA-Seq based only on gene expression data. Overall, inferring GRN from time-course bulk RNA-Seq is an effective approach to understand the regulatory relationships underlying biological events in multicellular organisms.

scholarly journals Robust inference of the context specific structure and temporal dynamics of gene regulatory network

BMC Genomics ◽  
2010 ◽  
Vol 11 (Suppl 3) ◽  
pp. S11 ◽  
Author(s):  
Jia Meng ◽  
Mingzhu Lu ◽  
Yidong Chen ◽  
Shou-Jiang Gao ◽  
Yufei Huang
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Temporal Dynamics ◽  
Specific Structure ◽  
Robust Inference ◽  
Gene Regulatory ◽  
Context Specific

scholarly journals A gene regulatory network to control EMT programs in development and disease

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Hassan Fazilaty ◽  
Luciano Rago ◽  
Khalil Kass Youssef ◽  
Oscar H. Ocaña ◽  
Francisco Garcia-Asencio ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Epithelial To Mesenchymal Transition ◽  
Good Prognosis ◽  
Vertebrate Development ◽  
Cell Plasticity ◽  
Mesenchymal Transition ◽  
Gene Regulatory

Abstract The Epithelial to Mesenchymal Transition (EMT) regulates cell plasticity during embryonic development and in disease. It is dynamically orchestrated by transcription factors (EMT-TFs), including Snail, Zeb, Twist and Prrx, all activated by TGF-β among other signals. Here we find that Snail1 and Prrx1, which respectively associate with gain or loss of stem-like properties and with bad or good prognosis in cancer patients, are expressed in complementary patterns during vertebrate development and in cancer. We show that this complementarity is established through a feedback loop in which Snail1 directly represses Prrx1, and Prrx1, through direct activation of the miR-15 family, attenuates the expression of Snail1. We also describe how this gene regulatory network can establish a hierarchical temporal expression of Snail1 and Prrx1 during EMT and validate its existence in vitro and in vivo, providing a mechanism to switch and select different EMT programs with important implications in development and disease.

A Method to Construct Gene Regulatory Networks to Estimate and Calculate Time Delays

2013 ◽  
Vol 62 (1) ◽  
Author(s):  
Mohd Saberi Mohamad ◽  
Chai Suk Phin
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Time Delays ◽  
Time Course ◽  
Target Gene ◽  
Biological Research ◽  
Expression Change ◽  
Gene Regulatory ◽  
Regulator Genes

In general, the motive of this research is to infer gene regulatory network in order to clarify the basis consequences of biological process at the molecular level. Time course gene expression profiling dataset has been widely used in basic biological research, especially in transcription regulation studies since the microarray dataset is a short time course gene expression dataset and have lots of errors, missing value, and noise.  In this research, R library is implemented in this method to construct gene regulatory which aims to estimate and calculate the time delays between genes and transcription factor. Time delay is the parameters of the modeled time delay linear regression models and a time lag during gene expression change of the regulator genes toward target gene expression. The constructed gene regulatory network provided information of time delays between expression change in regulator genes and its target gene which can be applied to investigate important time-related biological process in cells. The result of time delays and regulation patterns in gene regulatory network may contribute into biological research such as cell development, cell cycle, and cell differentiation in any of living cells.

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