scholarly journals The dynamic gene expression patterns of transcription factors constituting the sea urchin aboral ectoderm gene regulatory network

2010 ◽  
Vol 240 (1) ◽  
pp. 250-260 ◽  
Author(s):  
Jen-Hao Chen ◽  
Yi-Jyun Luo ◽  
Yi-Hsien Su
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Gene Regulatory Network ◽  
Sea Urchin ◽  
Regulatory Network ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Gene Regulatory

scholarly journals Developmental gene regulatory networks in sea urchins and what we can learn from them

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 203 ◽  
Author(s):  
Megan L. Martik ◽  
Deirdre C. Lyons ◽  
David R. McClay
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Network ◽  
Sea Urchin ◽  
Network Model ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Sea Urchins ◽  
Cell Types ◽  
Zygotic Transcription ◽  
Gene Regulatory

Sea urchin embryos begin zygotic transcription shortly after the egg is fertilized.  Throughout the cleavage stages a series of transcription factors are activated and, along with signaling through a number of pathways, at least 15 different cell types are specified by the beginning of gastrulation.  Experimentally, perturbation of contributing transcription factors, signals and receptors and their molecular consequences enabled the assembly of an extensive gene regulatory network model.  That effort, pioneered and led by Eric Davidson and his laboratory, with many additional insights provided by other laboratories, provided the sea urchin community with a valuable resource.  Here we describe the approaches used to enable the assembly of an advanced gene regulatory network model describing molecular diversification during early development.  We then provide examples to show how a relatively advanced authenticated network can be used as a tool for discovery of how diverse developmental mechanisms are controlled and work.

scholarly journals Regulation of transcription reactivation dynamics exiting mitosis

2021 ◽  
Vol 17 (10) ◽  
pp. e1009354
Author(s):  
Sergio Sarnataro ◽  
Andrea Riba ◽  
Nacho Molina
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Control Cell ◽  
Core Gene ◽  
Regulation Of Transcription ◽  
Gene Regulatory

Proliferating cells experience a global reduction of transcription during mitosis, yet their cell identity is maintained and regulatory information is propagated from mother to daughter cells. Mitotic bookmarking by transcription factors has been proposed as a potential mechanism to ensure the reactivation of transcription at the proper set of genes exiting mitosis. Recently, mitotic transcription and waves of transcription reactivation have been observed in synchronized populations of human hepatoma cells. However, the study did not consider that mitotic-arrested cell populations progressively desynchronize leading to measurements of gene expression on a mixture of cells at different internal cell-cycle times. Moreover, it is not well understood yet what is the precise role of mitotic bookmarking on mitotic transcription as well as on the transcription reactivation waves. Ultimately, the core gene regulatory network driving the precise transcription reactivation dynamics remains to be identified. To address these questions, we developed a mathematical model to correct for the progressive desynchronization of cells and estimate gene expression dynamics with respect to a cell-cycle pseudotime. Furthermore, we used a multiple linear regression model to infer transcription factor activity dynamics. Our analysis allows us to characterize waves of transcription factor activities exiting mitosis and predict a core gene regulatory network responsible of the transcription reactivation dynamics. Moreover, we identified more than 60 transcription factors that are highly active during mitosis and represent new candidates of mitotic bookmarking factors which could be relevant therapeutic targets to control cell proliferation.

scholarly journals Segmentation and patterning of the vertebrate hindbrain

Development ◽  
2021 ◽  
Vol 148 (15) ◽  
Author(s):  
Robb Krumlauf ◽  
David G. Wilkinson
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Dynamic Regulation ◽  
Regulatory Cascade ◽  
Gene Regulatory ◽  
Key Aspects ◽  
Cell Segregation ◽  
A Site

ABSTRACT During early development, the hindbrain is sub-divided into rhombomeres that underlie the organisation of neurons and adjacent craniofacial tissues. A gene regulatory network of signals and transcription factors establish and pattern segments with a distinct anteroposterior identity. Initially, the borders of segmental gene expression are imprecise, but then become sharply defined, and specialised boundary cells form. In this Review, we summarise key aspects of the conserved regulatory cascade that underlies the formation of hindbrain segments. We describe how the pattern is sharpened and stabilised through the dynamic regulation of cell identity, acting in parallel with cell segregation. Finally, we discuss evidence that boundary cells have roles in local patterning, and act as a site of neurogenesis within the hindbrain.

scholarly journals Developmental gene regulatory network connections predicted by machine learning from gene expression data alone

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261926
Author(s):  
Jingyi Zhang ◽  
Farhan Ibrahim ◽  
Emily Najmulski ◽  
George Katholos ◽  
Doaa Altarawy ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Gene Regulatory Network ◽  
Sea Urchin ◽  
Gene Expression Data ◽  
Regulatory Network ◽  
Machine Learning Algorithms ◽  
Gene Interactions ◽  
Expression Data ◽  
Gene Regulatory

Gene regulatory network (GRN) inference can now take advantage of powerful machine learning algorithms to complement traditional experimental methods in building gene networks. However, the dynamical nature of embryonic development–representing the time-dependent interactions between thousands of transcription factors, signaling molecules, and effector genes–is one of the most challenging arenas for GRN prediction. In this work, we show that successful GRN predictions for a developmental network from gene expression data alone can be obtained with the Priors Enriched Absent Knowledge (PEAK) network inference algorithm. PEAK is a noise-robust method that models gene expression dynamics via ordinary differential equations and selects the best network based on information-theoretic criteria coupled with the machine learning algorithm Elastic Net. We test our GRN prediction methodology using two gene expression datasets for the purple sea urchin, Stronglyocentrotus purpuratus, and cross-check our results against existing GRN models that have been constructed and validated by over 30 years of experimental results. Our results find a remarkably high degree of sensitivity in identifying known gene interactions in the network (maximum 81.58%). We also generate novel predictions for interactions that have not yet been described, which provide a resource for researchers to use to further complete the sea urchin GRN. Published ChIPseq data and spatial co-expression analysis further support a subset of the top novel predictions. We conclude that GRN predictions that match known gene interactions can be produced using gene expression data alone from developmental time series experiments.

scholarly journals Integrated Epigenetic Mapping of Human and Mouse Salivary Gene Regulation

2018 ◽  
Vol 98 (2) ◽  
pp. 209-217 ◽  
Author(s):  
D.G. Michael ◽  
T.J.F. Pranzatelli ◽  
B.M. Warner ◽  
H. Yin ◽  
J.A. Chiorini
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Salivary Gland ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Factors Associated ◽  
Genome Wide ◽  
Gene Regulatory ◽  
Cis And Trans ◽  
Genomic Footprinting

Significant effort has been applied to identify the genome-wide gene expression profiles associated with salivary gland development and pathophysiology. However, relatively little is known about the regulators that control salivary gland gene expression. We integrated data from DNase1 digital genomic footprinting, RNA-seq, and gene expression microarrays to comprehensively characterize the cis- and trans-regulatory components controlling gene expression of the healthy submandibular salivary gland. Analysis of 32 human tissues and 87 mouse tissues was performed to identify the highly expressed and tissue-enriched transcription factors driving salivary gland gene expression. Following RNA analysis, protein expression levels and subcellular localization of 39 salivary transcription factors were confirmed by immunohistochemistry. These expression analyses revealed that the salivary gland highly expresses transcription factors associated with endoplasmic reticulum stress, human T-cell lymphotrophic virus 1 expression, and Epstein-Barr virus reactivation. DNase1 digital genomic footprinting to a depth of 333,426,353 reads was performed and utilized to generate a salivary gland gene regulatory network describing the genome-wide chromatin accessibility and transcription factor binding of the salivary gland at a single-nucleotide resolution. Analysis of the DNase1 gene regulatory network identified dense interconnectivity among PLAG1, MYB, and 13 other transcription factors associated with balanced chromosomal translocations and salivary gland tumors. Collectively, these analyses provide a comprehensive atlas of the cis- and trans-regulators of the salivary gland and highlight known aberrantly regulated pathways of diseases affecting the salivary glands.

scholarly journals Regulation of transcription reactivation dynamics exiting mitosis

2020 ◽  
Author(s):  
Sergio Sarnataro ◽  
Andrea Riba ◽  
Nacho Molina
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Control Cell ◽  
Core Gene ◽  
Regulation Of Transcription ◽  
Gene Regulatory

AbstractProliferating cells experience a global reduction of transcription during mitosis, yet their cell identity is maintained and regulatory information is propagated from mother to daughter cells. Mitotic bookmarking by transcription factors has been proposed as a potential mechanism to ensure the reactivation of transcription at the proper set of genes exiting mitosis. Recently, mitotic transcription and waves of transcription reactivation have been observed in synchronized populations of human hepatoma cells. However, the study did not consider that mitotic-arrested cell populations progressively desynchronize leading to measurements of gene expression on a mixture of cells at different internal cell-cycle times. Moreover, it is not well understood yet what is the precise role of mitotic bookmarking on mitotic transcription as well as on the transcription reactivation waves. Ultimately, the core gene regulatory network driving the precise transcription reactivation dynamics remains to be identified. To address these questions, we developed a mathematical model to correct for the progressive desynchronization of cells and estimate gene expression dynamics with respect to a cell-cycle pseudotime. Furthermore, we used a multiple linear regression model to infer transcription factor activity dynamics. Our analysis allows us to characterize waves of transcription factor activities exiting mitosis and identify a core gene regulatory network responsible of the transcription reactivation dynamics. Moreover, we identified more than 60 transcription factors that are highly active during mitosis and represent new candidates of mitotic bookmarking factors which could represent relevant therapeutic targets to control cell proliferation.

scholarly journals Study on the Relationship between the miRNA-centered ceRNA Regulatory Network and Fatigue

2021 ◽  
Author(s):  
Xingzhe Yang ◽  
Feng Li ◽  
Jie Ma ◽  
Yan Liu ◽  
Xuejiao Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Hot Spot ◽  
Genetic Research ◽  
Noncoding Rnas ◽  
Messenger Rnas ◽  
Effective Prevention ◽  
Gene Regulatory ◽  
The Relationship

AbstractIn recent years, the incidence of fatigue has been increasing, and the effective prevention and treatment of fatigue has become an urgent problem. As a result, the genetic research of fatigue has become a hot spot. Transcriptome-level regulation is the key link in the gene regulatory network. The transcriptome includes messenger RNAs (mRNAs) and noncoding RNAs (ncRNAs). MRNAs are common research targets in gene expression profiling. Noncoding RNAs, including miRNAs, lncRNAs, circRNAs and so on, have been developed rapidly. Studies have shown that miRNAs are closely related to the occurrence and development of fatigue. MiRNAs can regulate the immune inflammatory reaction in the central nervous system (CNS), regulate the transmission of nerve impulses and gene expression, regulate brain development and brain function, and participate in the occurrence and development of fatigue by regulating mitochondrial function and energy metabolism. LncRNAs can regulate dopaminergic neurons to participate in the occurrence and development of fatigue. This has certain value in the diagnosis of chronic fatigue syndrome (CFS). CircRNAs can participate in the occurrence and development of fatigue by regulating the NF-κB pathway, TNF-α and IL-1β. The ceRNA hypothesis posits that in addition to the function of miRNAs in unidirectional regulation, mRNAs, lncRNAs and circRNAs can regulate gene expression by competitive binding with miRNAs, forming a ceRNA regulatory network with miRNAs. Therefore, we suggest that the miRNA-centered ceRNA regulatory network is closely related to fatigue. At present, there are few studies on fatigue-related ncRNA genes, and most of these limited studies are on miRNAs in ncRNAs. However, there are a few studies on the relationship between lncRNAs, cirRNAs and fatigue. Less research is available on the pathogenesis of fatigue based on the ceRNA regulatory network. Therefore, exploring the complex mechanism of fatigue based on the ceRNA regulatory network is of great significance. In this review, we summarize the relationship between miRNAs, lncRNAs and circRNAs in ncRNAs and fatigue, and focus on exploring the regulatory role of the miRNA-centered ceRNA regulatory network in the occurrence and development of fatigue, in order to gain a comprehensive, in-depth and new understanding of the essence of the fatigue gene regulatory network.

scholarly journals Inference of the Xenopus tropicalis embryonic regulatory network and spatial gene expression patterns

2014 ◽  
Vol 8 (1) ◽  
pp. 3 ◽  
Author(s):  
Zhenzhen Zheng ◽  
Scott Christley ◽  
William T Chiu ◽  
Ira L Blitz ◽  
Xiaohui Xie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Regulatory Network ◽  
Expression Patterns ◽  
Xenopus Tropicalis ◽  
Gene Expression Patterns
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