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Buffered Qualitative Stability explains the robustness and evolvability of transcriptional networks.

Albergante L, Blow JJ, Newman TJ - Elife (2014)

Bottom Line: The gene regulatory network (GRN) is the central decision-making module of the cell.BQS explains many of the small- and large-scale properties of GRNs, provides conditions for evolvable robustness, and highlights general features of transcriptional response.BQS is severely compromised in a human cancer cell line, suggesting that loss of BQS might underlie the phenotypic plasticity of cancer cells, and highlighting a possible sequence of GRN alterations concomitant with cancer initiation.

View Article: PubMed Central - PubMed

Affiliation: College of Life Sciences, University of Dundee, Dundee, United Kingdom l.albergante@dundee.ac.uk.

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Incomplete feedback loops in P. aeruginosa, M. tuberculosis and other yeast datasets.Number of incomplete feedback loops is provided on a logarithmic scale for M. tuberculosis (A), P. aeruginosa (B), the yeast dataset derived from Lee et al. (C), the yeast dataset derived from Luscombe et al. (D), and the yeast dataset derived from MacIsaac et al. (E). In each case the real dataset is compared with a randomly simulated network containing the same number of nodes and connections. For the random networks, each graph reports the mean and standard deviation. Note how the number of incomplete feedback loops decreases rapidly for the more validated datasets (A–C).DOI:http://dx.doi.org/10.7554/eLife.02863.010
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fig3s1: Incomplete feedback loops in P. aeruginosa, M. tuberculosis and other yeast datasets.Number of incomplete feedback loops is provided on a logarithmic scale for M. tuberculosis (A), P. aeruginosa (B), the yeast dataset derived from Lee et al. (C), the yeast dataset derived from Luscombe et al. (D), and the yeast dataset derived from MacIsaac et al. (E). In each case the real dataset is compared with a randomly simulated network containing the same number of nodes and connections. For the random networks, each graph reports the mean and standard deviation. Note how the number of incomplete feedback loops decreases rapidly for the more validated datasets (A–C).DOI:http://dx.doi.org/10.7554/eLife.02863.010

Mentions: In principle the Qualitative Stability observed in GRNs might be easy to break by addition of another link to the network. For example, a long feedback loop can be created by the addition of a feedback connection from a TF lower down in a network path to a TF higher up in that path. This could occur, for example, through a mutation in the promoter of the target gene allowing it to be bound by a new TF. It is also possible that stress conditions could cause TFs to act inappropriately at promoters they do not normally regulate. In this way Qualitative Stability could be lost, and GRNs could become unstable. Thus, we predict that if long feedback loops are detrimental because of their instability, then GRNs would be configured to minimize destabilization via the addition of new connections. We call network paths that can be transformed into loops by the addition of a single new link ‘incomplete feedback loops’ (Figure 3D–E). The abundance of incomplete feedback loops in the GRNs of E. coli, S. cerevisiae and human is shown in Figure 3A–C (lightly shaded bars). Data for M. tuberculosis and P. aeruginosa are shown in Figure 3—figure supplement 1A,B. For each of these GRNs there are <2000 incomplete feedback loops and they tend to be of a relatively small size. A similar empirical observation has been made regarding transcriptional cascades (Rosenfeld and Alon, 2003; The modENCODE Consortium et al., 2010). This is in stark contrast to comparable random networks (Figure 3A–C, Figure 3—figure supplement 1A,B, heavily shaded bars, and Figure 3—figure supplement 4), which on average have a thousand-fold greater number of incomplete feedback loops (>105) of a significantly larger size. Statistical analyses suggest that there is an extremely small probability (<10−19) that the absence of long incomplete feedback loops is a chance event in the three organisms considered (Figure 3—figure supplement 2A–C). These results are relatively robust to variations in the confidence levels of the E. coli and S. cerevisiae GRN (Figure 3—figure supplement 3A,B), and remain valid when different random models are considered (Figure 3—figure supplement 4). Note that the distribution of incomplete feedback loops is indicative of the different topological structures that can be observed in the network, and is not necessarily monotonically decreasing (Figure 3—figure supplement 5).10.7554/eLife.02863.009Figure 3.Incomplete feedback loops in real and simulated GRNs.


Buffered Qualitative Stability explains the robustness and evolvability of transcriptional networks.

Albergante L, Blow JJ, Newman TJ - Elife (2014)

Incomplete feedback loops in P. aeruginosa, M. tuberculosis and other yeast datasets.Number of incomplete feedback loops is provided on a logarithmic scale for M. tuberculosis (A), P. aeruginosa (B), the yeast dataset derived from Lee et al. (C), the yeast dataset derived from Luscombe et al. (D), and the yeast dataset derived from MacIsaac et al. (E). In each case the real dataset is compared with a randomly simulated network containing the same number of nodes and connections. For the random networks, each graph reports the mean and standard deviation. Note how the number of incomplete feedback loops decreases rapidly for the more validated datasets (A–C).DOI:http://dx.doi.org/10.7554/eLife.02863.010
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4151086&req=5

fig3s1: Incomplete feedback loops in P. aeruginosa, M. tuberculosis and other yeast datasets.Number of incomplete feedback loops is provided on a logarithmic scale for M. tuberculosis (A), P. aeruginosa (B), the yeast dataset derived from Lee et al. (C), the yeast dataset derived from Luscombe et al. (D), and the yeast dataset derived from MacIsaac et al. (E). In each case the real dataset is compared with a randomly simulated network containing the same number of nodes and connections. For the random networks, each graph reports the mean and standard deviation. Note how the number of incomplete feedback loops decreases rapidly for the more validated datasets (A–C).DOI:http://dx.doi.org/10.7554/eLife.02863.010
Mentions: In principle the Qualitative Stability observed in GRNs might be easy to break by addition of another link to the network. For example, a long feedback loop can be created by the addition of a feedback connection from a TF lower down in a network path to a TF higher up in that path. This could occur, for example, through a mutation in the promoter of the target gene allowing it to be bound by a new TF. It is also possible that stress conditions could cause TFs to act inappropriately at promoters they do not normally regulate. In this way Qualitative Stability could be lost, and GRNs could become unstable. Thus, we predict that if long feedback loops are detrimental because of their instability, then GRNs would be configured to minimize destabilization via the addition of new connections. We call network paths that can be transformed into loops by the addition of a single new link ‘incomplete feedback loops’ (Figure 3D–E). The abundance of incomplete feedback loops in the GRNs of E. coli, S. cerevisiae and human is shown in Figure 3A–C (lightly shaded bars). Data for M. tuberculosis and P. aeruginosa are shown in Figure 3—figure supplement 1A,B. For each of these GRNs there are <2000 incomplete feedback loops and they tend to be of a relatively small size. A similar empirical observation has been made regarding transcriptional cascades (Rosenfeld and Alon, 2003; The modENCODE Consortium et al., 2010). This is in stark contrast to comparable random networks (Figure 3A–C, Figure 3—figure supplement 1A,B, heavily shaded bars, and Figure 3—figure supplement 4), which on average have a thousand-fold greater number of incomplete feedback loops (>105) of a significantly larger size. Statistical analyses suggest that there is an extremely small probability (<10−19) that the absence of long incomplete feedback loops is a chance event in the three organisms considered (Figure 3—figure supplement 2A–C). These results are relatively robust to variations in the confidence levels of the E. coli and S. cerevisiae GRN (Figure 3—figure supplement 3A,B), and remain valid when different random models are considered (Figure 3—figure supplement 4). Note that the distribution of incomplete feedback loops is indicative of the different topological structures that can be observed in the network, and is not necessarily monotonically decreasing (Figure 3—figure supplement 5).10.7554/eLife.02863.009Figure 3.Incomplete feedback loops in real and simulated GRNs.

Bottom Line: The gene regulatory network (GRN) is the central decision-making module of the cell.BQS explains many of the small- and large-scale properties of GRNs, provides conditions for evolvable robustness, and highlights general features of transcriptional response.BQS is severely compromised in a human cancer cell line, suggesting that loss of BQS might underlie the phenotypic plasticity of cancer cells, and highlighting a possible sequence of GRN alterations concomitant with cancer initiation.

View Article: PubMed Central - PubMed

Affiliation: College of Life Sciences, University of Dundee, Dundee, United Kingdom l.albergante@dundee.ac.uk.

Show MeSH
Related in: MedlinePlus