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Understanding key features of bacterial restriction-modification systems through quantitative modeling

Overview of attention for article published in BMC Systems Biology, February 2017
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Title
Understanding key features of bacterial restriction-modification systems through quantitative modeling
Published in
BMC Systems Biology, February 2017
DOI 10.1186/s12918-016-0377-x
Pubmed ID
Authors

Andjela Rodic, Bojana Blagojevic, Evgeny Zdobnov, Magdalena Djordjevic, Marko Djordjevic

Abstract

Restriction-modification (R-M) systems are rudimentary bacterial immune systems. The main components include restriction enzyme (R), which cuts specific unmethylated DNA sequences, and the methyltransferase (M), which protects the same DNA sequences. The expression of R-M system components is considered to be tightly regulated, to ensure successful establishment in a naïve bacterial host. R-M systems are organized in different architectures (convergent or divergent) and are characterized by different features, i.e. binding cooperativities, dissociation constants of dimerization, translation rates, which ensure this tight regulation. It has been proposed that R-M systems should exhibit certain dynamical properties during the system establishment, such as: i) a delayed expression of R with respect to M, ii) fast transition of R from "OFF" to "ON" state, iii) increased stability of the toxic molecule (R) steady-state levels. It is however unclear how different R-M system features and architectures ensure these dynamical properties, particularly since it is hard to address this question experimentally. To understand design of different R-M systems, we computationally analyze two R-M systems, representative of the subset controlled by small regulators called 'C proteins', and differing in having convergent or divergent promoter architecture. We show that, in the convergent system, abolishing any of the characteristic system features adversely affects the dynamical properties outlined above. Moreover, an extreme binding cooperativity, accompanied by a very high dissociation constant of dimerization, observed in the convergent system, but absent from other R-M systems, can be explained in terms of the same properties. Furthermore, we develop the first theoretical model for dynamics of a divergent R-M system, which does not share any of the convergent system features, but has overlapping promoters. We show that i) the system dynamics exhibits the same three dynamical properties, ii) introducing any of the convergent system features to the divergent system actually diminishes these properties. Our results suggest that different R-M architectures and features may be understood in terms of constraints imposed by few simple dynamical properties of the system, providing a unifying framework for understanding these seemingly diverse systems. We also provided predictions for the perturbed R-M systems dynamics, which may in future be tested through increasingly available experimental techniques, such as re-engineering R-M systems and single-cell experiments.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 23%
Student > Ph. D. Student 8 20%
Researcher 5 13%
Student > Bachelor 3 8%
Student > Doctoral Student 2 5%
Other 4 10%
Unknown 9 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 13 33%
Agricultural and Biological Sciences 6 15%
Immunology and Microbiology 3 8%
Medicine and Dentistry 2 5%
Computer Science 2 5%
Other 4 10%
Unknown 10 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 May 2017.
All research outputs
#8,593,182
of 11,194,639 outputs
Outputs from BMC Systems Biology
#653
of 966 outputs
Outputs of similar age
#172,841
of 264,700 outputs
Outputs of similar age from BMC Systems Biology
#13
of 15 outputs
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