scholarly journals Bioinformatics Applications to Reveal Molecular Mechanisms of Gene Expression Regulation in Model Organisms

2021 ◽  
Vol 22 (21) ◽  
pp. 11973
Author(s):  
Yuriy L. Orlov ◽  
Tatiana V. Tatarinova ◽  
Anastasia A. Anashkina
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Gene Expression Regulation ◽  
Expression Regulation ◽  
Model Organisms ◽  
Complex Phenomenon ◽  
Tissue Levels ◽  
Bioinformatics Tools

Gene expression regulation at the transcriptome, genome, cell, and tissue levels is a complex phenomenon demanding the development of bioinformatics tools [...]

scholarly journals Mechanically Sensitive HSF1 is a Key Regulator of Left-Right Symmetry Breaking in Zebrafish Embryos

2021 ◽  
Author(s):  
Jing Du ◽  
Shu-Kai Li ◽  
Liu-Yuan Guan ◽  
Zheng Guo ◽  
Jiang-Fan Yin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Symmetry Breaking ◽  
Molecular Mechanisms ◽  
Fluid Shear Stress ◽  
Gene Expression Regulation ◽  
Early Stage ◽  
Expression Regulation ◽  
Zebrafish Embryos ◽  
Mechanical Stimuli ◽  
Nodal Flow

AbstractThe left-right symmetry breaking of vertebrate embryos requires fluid flow (called nodal flow in zebrafish). However, the molecular mechanisms that mediate the asymmetric gene expression regulation under nodal flow remain elusive. In this paper, we report that heat shock factor 1 (HSF1) is asymmetrically activated in the Kuppfer’s vesicle at the early stage of zebrafish embryos in the presence of nodal flow. Deficiency in HSF1 expression caused a significant situs inversus and disrupted gene expression asymmetry of nodal signaling proteins in zebrafish embryos. Further studies demonstrated that HSF1 could be immediately activated by fluid shear stress. The mechanical sensation ability of HSF1 is conserved in a variety of mechanical stimuli in different cell types. Moreover, cilia and the Ca2+-Akt signaling axis are essential for the activation of HSF1 under mechanical stress in vitro and in vivo. Considering the conserved expression of HSF1 in organisms, these findings unveil a fundamental mechanism of gene expression regulation triggered by mechanical clues during embryonic development and other physiological and pathological transformations.

Gerea: Prediction Of Gene Expression Regulators From Transcriptome Profiling Data To Transition Networks

2021 ◽  
Vol 16 ◽  
Author(s):  
Min Yao ◽  
Caiyun Jiang ◽  
Chenglong Li ◽  
Yongxia Li ◽  
Shan Jiang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Target Gene ◽  
Gene Expression Regulation ◽  
Transcriptome Profiling ◽  
Expression Regulation ◽  
Mouse Gene ◽  
Mouse Gene Expression ◽  
Causality Inference ◽  
Identify Gene Expression

Background: Mammalian genes are regulated at the transcriptional and post-transcriptional levels. These mechanisms may involve the direct promotion or inhibition of transcription via a regulator or post-transcriptional regulation through factors such as micro (mi)RNAs. Objective: This study aimed to construct gene regulation relationships modulated by causality inference-based miRNA-(transition factor)-(target gene) networks and analyze gene expression data to identify gene expression regulators. Methods: Mouse gene expression regulation relationships were manually curated from literature using a text mining method which was then employed to generate miRNA-(transition factor)-(target gene) networks. An algorithm was then introduced to identify gene expression regulators from transcriptome profiling data by applying enrichment analysis to these networks. Results: A total of 22,271 mouse gene expression regulation relationships were curated for 4,018 genes and 242 miRNAs. GEREA software was developed to perform the integrated analyses. We applied the algorithm to transcriptome data for synthetic miR-155 oligo-treated mouse CD4+ T-cells and confirmed that miR-155 is an important network regulator. The software was also tested on publicly available transcriptional profiling data for Salmonella infection, resulting in the identification of miR-125b as an important regulator. Conclusion: The causality inference-based miRNA-(transition factor)-(target gene) networks serve as a novel resource for gene expression regulation research, and GEREA is an effective and useful adjunct to the currently available methods. The regulatory networks and the algorithm implemented in the GEREA software package are available under a free academic license at website : http://www.thua45.cn/gerea.

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