scholarly journals An Integrative Developmental Genomics and Systems Biology Approach to Identify an In Vivo Sox Trio-Mediated Gene Regulatory Network in Murine Embryos

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Wenqing Jean Lee ◽  
Sumantra Chatterjee ◽  
Sook Peng Yap ◽  
Siew Lan Lim ◽  
Xing Xing ◽  
...  
Keyword(s):  
Systems Biology ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Cell Types ◽  
Specific Cell ◽  
Morpholino Knockdown ◽  
Cell Type Specific ◽  
Gene Regulatory

Embryogenesis is an intricate process involving multiple genes and pathways. Some of the key transcription factors controlling specific cell types are the Sox trio, namely, Sox5, Sox6, and Sox9, which play crucial roles in organogenesis working in a concerted manner. Much however still needs to be learned about their combinatorial roles during this process. A developmental genomics and systems biology approach offers to complement the reductionist methodology of current developmental biology and provide a more comprehensive and integrated view of the interrelationships of complex regulatory networks that occur during organogenesis. By combining cell type-specific transcriptome analysis and in vivo ChIP-Seq of the Sox trio using mouse embryos, we provide evidence for the direct control of Sox5 and Sox6 by the transcriptional trio in the murine model and by Morpholino knockdown in zebrafish and demonstrate the novel role of Tgfb2, Fbxl18, and Tle3 in formation of Sox5, Sox6, and Sox9 dependent tissues. Concurrently, a complete embryonic gene regulatory network has been generated, identifying a wide repertoire of genes involved and controlled by the Sox trio in the intricate process of normal embryogenesis.

scholarly journals Improving gene regulatory network structure using redundancy reduction in the MRNET algorithm

RSC Advances ◽  
2017 ◽  
Vol 7 (37) ◽  
pp. 23222-23233 ◽  
Author(s):  
Wei Liu ◽  
Wen Zhu ◽  
Bo Liao ◽  
Haowen Chen ◽  
Siqi Ren ◽  
...  
Keyword(s):  
Systems Biology ◽  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Network Structure ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Central Problem ◽  
Expression Data ◽  
Redundancy Reduction ◽  
Gene Regulatory

Inferring gene regulatory networks from expression data is a central problem in systems biology.

scholarly journals Insights Into the Early Gene Regulatory Network Controlling Neural Crest and Placode Fate Choices at the Neural Border

2020 ◽  
Vol 11 ◽  
Author(s):  
Subham Seal ◽  
Anne H. Monsoro-Burq
Keyword(s):  
Neural Crest ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Cell Types ◽  
Model Systems ◽  
Early Gene ◽  
Secretory Cells ◽  
Peripheral Neurons ◽  
Gene Regulatory

The neural crest (NC) cells and cranial placodes are two ectoderm-derived innovations in vertebrates that led to the acquisition of a complex head structure required for a predatory lifestyle. They both originate from the neural border (NB), a portion of the ectoderm located between the neural plate (NP), and the lateral non-neural ectoderm. The NC gives rise to a vast array of tissues and cell types such as peripheral neurons and glial cells, melanocytes, secretory cells, and cranial skeletal and connective cells. Together with cells derived from the cranial placodes, which contribute to sensory organs in the head, the NC also forms the cranial sensory ganglia. Multiple in vivo studies in different model systems have uncovered the signaling pathways and genetic factors that govern the positioning, development, and differentiation of these tissues. In this literature review, we give an overview of NC and placode development, focusing on the early gene regulatory network that controls the formation of the NB during early embryonic stages, and later dictates the choice between the NC and placode progenitor fates.

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 Estimating Drivers of Cell State Transitions Using Gene Regulatory Network Models

2016 ◽  
Author(s):  
Daniel Schlauch ◽  
Kimberly Glass ◽  
Craig P. Hersh ◽  
Edwin K. Silverman ◽  
John Quackenbush
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Expression Patterns ◽  
Cell Types ◽  
State Transitions ◽  
Chronic Obstructive ◽  
Simple Extension ◽  
Cell State ◽  
Gene Regulatory

AbstractSpecific cellular states are often associated with distinct gene expression patterns. These states are plastic, changing during development, or in the transition from health to disease. One relatively simple extension of this concept is to recognize that we can classify different cell-types by their active gene regulatory networks and that, consequently, transitions between cellular states can be modeled by changes in these underlying regulatory networks. Here we describe MONSTER, MOdeling Network State Transitions from Expression and Regulatory data, a regression-based method for inferring transcription factor drivers of cell state conditions at the gene regulatory network level. As a demonstration, we apply MONSTER to four different studies of chronic obstructive pulmonary disease to identify transcription factors that alter the network structure as the cell state progresses toward the disease-state. Our results demonstrate that MONSTER can find strong regulatory signals that persist across studies and tissues of the same disease and that are not detectable using conventional analysis methods based on differential expression. An R package implementing MONSTER is available at github.com/QuackenbushLab/MONSTER.

scholarly journals MITO-Tag Mice enable rapid isolation and multimodal profiling of mitochondria from specific cell types in vivo

2018 ◽  
Vol 116 (1) ◽  
pp. 303-312 ◽  
Author(s):  
Erol C. Bayraktar ◽  
Lou Baudrier ◽  
Ceren Özerdem ◽  
Caroline A. Lewis ◽  
Sze Ham Chan ◽  
...  
Keyword(s):  
Metabolite Profiling ◽  
Cultured Cells ◽  
Transgenic Animals ◽  
Cell Types ◽  
Tissue Level ◽  
Specific Cell ◽  
Mammalian Tissues ◽  
Cell Type Specific ◽  
Untargeted Metabolite Profiling

Mitochondria are metabolic organelles that are essential for mammalian life, but the dynamics of mitochondrial metabolism within mammalian tissues in vivo remains incompletely understood. While whole-tissue metabolite profiling has been useful for studying metabolism in vivo, such an approach lacks resolution at the cellular and subcellular level. In vivo methods for interrogating organellar metabolites in specific cell types within mammalian tissues have been limited. To address this, we built on prior work in which we exploited a mitochondrially localized 3XHA epitope tag (MITO-Tag) for the fast isolation of mitochondria from cultured cells to generate MITO-Tag Mice. Affording spatiotemporal control over MITO-Tag expression, these transgenic animals enable the rapid, cell-type-specific immunoisolation of mitochondria from tissues, which we verified using a combination of proteomic and metabolomic approaches. Using MITO-Tag Mice and targeted and untargeted metabolite profiling, we identified changes during fasted and refed conditions in a diverse array of mitochondrial metabolites in hepatocytes and found metabolites that behaved differently at the mitochondrial versus whole-tissue level. MITO-Tag Mice should have utility for studying mitochondrial physiology, and our strategy should be generally applicable for studying other mammalian organelles in specific cell types in vivo.

scholarly journals MITO-Tag Mice enable rapid isolation and multimodal profiling of mitochondria from specific cell types in vivo

2018 ◽  
Author(s):  
Erol Can Bayraktar ◽  
Lou Baudrier ◽  
Ceren Özerdem ◽  
Caroline A. Lewis ◽  
Sze Ham Chan ◽  
...  
Keyword(s):  
Metabolite Profiling ◽  
Cultured Cells ◽  
Transgenic Animals ◽  
Cell Types ◽  
Tissue Level ◽  
Specific Cell ◽  
Mammalian Tissues ◽  
Cell Type Specific ◽  
Untargeted Metabolite Profiling

ABSTRACTMitochondria are metabolic organelles that are essential for mammalian life, but the dynamics of mitochondrial metabolism within mammalian tissues in vivo remains incompletely understood. While whole-tissue metabolite profiling has been useful for studying metabolism in vivo, such an approach lacks resolution at the cellular and subcellular level. In vivo methods for interrogating organellar metabolites in specific cell-types within mammalian tissues have been limited. To address this, we built on prior work in which we exploited a mitochondrially-localized 3XHA epitope-tag (“MITO-Tag”) for the fast isolation of mitochondria from cultured cells to now generate “MITO-Tag Mice.” Affording spatiotemporal control over MITO-Tag expression, these transgenic animals enable the rapid, cell-type-specific immunoisolation of mitochondria from tissues, which we verified using a combination of proteomic and metabolomic approaches. Using MITO-Tag Mice and targeted and untargeted metabolite profiling, we identified changes during fasted and refed conditions in a diverse array of mitochondrial metabolites in hepatocytes and found metabolites that behaved differently at the mitochondrial versus whole-tissue level. MITO-Tag Mice should have utility for studying mitochondrial physiology and our strategy should be generally applicable for studying other mammalian organelles in specific cell-types in vivo.

scholarly journals Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms

2019 ◽  
Author(s):  
Daniel Morgan ◽  
Matthew Studham ◽  
Andreas Tjärnberg ◽  
Holger Weishaupt ◽  
Fredrik J. Swartling ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Network Inference ◽  
Gene Regulatory Network Inference ◽  
Validation Data ◽  
Regulatory Interactions ◽  
Link Type ◽  
Gene Regulatory ◽  
Novel Interactions

AbstractThe gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease, which can lead to new therapies. Reliable inference of GRNs is however still a major challenge in systems biology.To deduce regulatory interactions relevant to cancer, we applied a recent computational inference framework to data from perturbation experiments in squamous carcinoma cell line A431. GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. We developed a novel approach to assess the predictiveness of inferred GRNs against validation data, despite the lack of a gold standard. The best GRN was significantly more predictive than the null model, both in crossvalidated benchmarks and for an independent dataset of the same genes under a different perturbation design. It agrees with many known links, in addition to predicting a large number of novel interactions from which a subset was experimentally validated. The inferred GRN captures regulatory interactions central to cancer-relevant processes and thus provides mechanistic insights that are useful for future cancer research.Data available at GSE125958Inferred GRNs and inference statistics available at https://dcolin.shinyapps.io/CancerGRN/ Software available at https://bitbucket.org/sonnhammergrni/genespider/src/BFECV/Author SummaryCancer is the second most common cause of death globally, and although cancer treatments have improved in recent years, we need to understand how regulatory mechanisms are altered in cancer to combat the disease efficiently. By applying gene perturbations and inference of gene regulatory networks to 40 genes known or suspected to have a role in cancer due to interactions with the oncogene MYC, we deduce their underlying regulatory interactions. Using a recent computational framework for inference together with a novel method for cross validation, we infer a reliable regulatory model of this system in a completely data driven manner, not reliant on literature or priors. The novel interactions add to the understanding of the progressive oncogenic regulatory process and may provide new targets for therapy.

scholarly journals A systems biology approach uncovers the core gene regulatory network governing iridophore fate choice from the neural crest

PLoS Genetics ◽  
2018 ◽  
Vol 14 (10) ◽  
pp. e1007402 ◽  
Author(s):  
Kleio Petratou ◽  
Tatiana Subkhankulova ◽  
James A. Lister ◽  
Andrea Rocco ◽  
Hartmut Schwetlick ◽  
...  
Keyword(s):  
Systems Biology ◽  
Neural Crest ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Core Gene ◽  
The Core ◽  
Gene Regulatory

PAGeneRN

2020 ◽  
pp. 1052-1075 ◽  
Author(s):  
Dina Elsayad ◽  
A. Ali ◽  
Howida A. Shedeed ◽  
Mohamed F. Tolba
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Network Analysis ◽  
Gene Regulatory Network ◽  
Gene Expression Data ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Expression Data ◽  
Gene Expression Data Analysis ◽  
Gene Regulatory

The gene expression analysis is an important research area of Bioinformatics. The gene expression data analysis aims to understand the genes interacting phenomena, gene functionality and the genes mutations effect. The Gene regulatory network analysis is one of the gene expression data analysis tasks. Gene regulatory network aims to study the genes interactions topological organization. The regulatory network is critical for understanding the pathological phenotypes and the normal cell physiology. There are many researches that focus on gene regulatory network analysis but unfortunately some algorithms are affected by data size. Where, the algorithm runtime is proportional to the data size, therefore, some parallel algorithms are presented to enhance the algorithms runtime and efficiency. This work presents a background, mathematical models and comparisons about gene regulatory networks analysis different techniques. In addition, this work proposes Parallel Architecture for Gene Regulatory Network (PAGeneRN).

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