scholarly journals Effects of Population Dynamics on Establishment of a Restriction-Modification System in a Bacterial Host

Molecules ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 198 ◽  
Author(s):  
Stefan Graovac ◽  
Andjela Rodic ◽  
Magdalena Djordjevic ◽  
Konstantin Severinov ◽  
Marko Djordjevic
Keyword(s):  
Gene Expression ◽  
Population Dynamics ◽  
Gene Expression Regulation ◽  
Expression Regulation ◽  
Dynamical Equations ◽  
Modeling Framework ◽  
Modification System ◽  
Individual Molecular Species ◽  
Intracellular Regulation ◽  
Restriction Modification

In vivo dynamics of protein levels in bacterial cells depend on both intracellular regulation and relevant population dynamics. Such population dynamics effects, e.g., interplay between cell and plasmid division rates, are, however, often neglected in modeling gene expression regulation. Including them in a model introduces additional parameters shared by the dynamical equations, which can significantly increase dimensionality of the parameter inference. We here analyse the importance of these effects, on a case of bacterial restriction-modification (R-M) system. We redevelop our earlier minimal model of this system gene expression regulation, based on a thermodynamic and dynamic system modeling framework, to include the population dynamics effects. To resolve the problem of effective coupling of the dynamical equations, we propose a “mean-field-like” procedure, which allows determining only part of the parameters at a time, by separately fitting them to expression dynamics data of individual molecular species. We show that including the interplay between kinetics of cell division and plasmid replication is necessary to explain the experimental measurements. Moreover, neglecting population dynamics effects can lead to falsely identifying non-existent regulatory mechanisms. Our results call for advanced methods to reverse-engineer intracellular regulation from dynamical data, which would also take into account the population dynamics effects.

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.

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.

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