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Gene Expression Plasticity Is Associated with Regulatory Complexity but Not with Specific Network Motifs
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Over the past two decades, a large body of theoretical and empirical work has been conducted with the aim of identifying the gene regulatory network topologies responsible for gene expression dynamics. Some studies have linked gene expression plasticity to specific network motifs such as feedforward loops, diamond motifs, or feedback loops. However, both theoretical and empirical work have produced equivocal results, as the same topologies have also been associated with expression robustness. As a step toward understanding the regulatory basis of gene expression plasticity, our goal is to understand how local regulatory topologies may contribute to it. To this end we compared theoretical predictions from a simulated network evolution model with empirical data based on Escherichia coli regulatory network. We investigated the link between network topology and gene expression at three levels: the number of regulators, the number and the proportion of loops (feedback loops, feedforward loops and diamond loops), and the proportion of unique motifs (here characterized by the position of up- or down-regulations within loops). Consistent results from our empirical and theoretical approaches revealed that plastic genes are, on average, regulated by a greater number of genes. In addition, our theoretical predictions showed that selection, as opposed to genetic drift, strongly biases the distribution of network motifs. However, similar results were observed when comparing plastic and non-plastic genes. Overall, this work illustrates that our current understanding of network topology may be insufficient to fully explain or predict gene expression plasticity.
Title: Gene Expression Plasticity Is Associated with Regulatory Complexity but Not with Specific Network Motifs
Description:
Over the past two decades, a large body of theoretical and empirical work has been conducted with the aim of identifying the gene regulatory network topologies responsible for gene expression dynamics.
Some studies have linked gene expression plasticity to specific network motifs such as feedforward loops, diamond motifs, or feedback loops.
However, both theoretical and empirical work have produced equivocal results, as the same topologies have also been associated with expression robustness.
As a step toward understanding the regulatory basis of gene expression plasticity, our goal is to understand how local regulatory topologies may contribute to it.
To this end we compared theoretical predictions from a simulated network evolution model with empirical data based on Escherichia coli regulatory network.
We investigated the link between network topology and gene expression at three levels: the number of regulators, the number and the proportion of loops (feedback loops, feedforward loops and diamond loops), and the proportion of unique motifs (here characterized by the position of up- or down-regulations within loops).
Consistent results from our empirical and theoretical approaches revealed that plastic genes are, on average, regulated by a greater number of genes.
In addition, our theoretical predictions showed that selection, as opposed to genetic drift, strongly biases the distribution of network motifs.
However, similar results were observed when comparing plastic and non-plastic genes.
Overall, this work illustrates that our current understanding of network topology may be insufficient to fully explain or predict gene expression plasticity.
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