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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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Feedback loops in M. tuberculosis, P. aeruginosa and other yeast datasets.Number of 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 genes, TFs and connections. For the random networks, each graph reports the mean and standard deviation. Note that Luscombe et al. (2004) was mainly concerned with the dynamics of yeast GRN and built the full GRN in such a way to better stress differences under different growth conditions. The dataset was constructed by adding to the network derived by Lee et al. (2002) new interactions derived under different experimental conditions, and the absence of a measure of confidence for the interactions makes it difficult to assess the strength of the experimental support for the interactions that form the feedback loops. The above considerations suggest that a moderate rate of false positives may be present in the dataset. The sensitivity of loop count to noise in the data therefore suggests that loop counting may not provide a good indication of the role of BQS by itself in this case. Moreover, MacIsaac et al. (2006) takes a computational approach to the construction of the GRN of S. cerevisiae using the experimental data provided by Harbison et al. (2004) as a starting point. Therefore, noisy data should be expected and caution should be used in interpreting the data. The network used was the most statistically restrictive. Although a limited number of long feedback loops is present in the dataset (E), comparison with random data indicates that this number is much smaller than expected by chance, still suggesting a selective pressure against instability. Note how the number of long feedback loops is extremely limited for the dataset derived from direct biological experimentation (A–C).DOI:http://dx.doi.org/10.7554/eLife.02863.005
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fig2s1: Feedback loops in M. tuberculosis, P. aeruginosa and other yeast datasets.Number of 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 genes, TFs and connections. For the random networks, each graph reports the mean and standard deviation. Note that Luscombe et al. (2004) was mainly concerned with the dynamics of yeast GRN and built the full GRN in such a way to better stress differences under different growth conditions. The dataset was constructed by adding to the network derived by Lee et al. (2002) new interactions derived under different experimental conditions, and the absence of a measure of confidence for the interactions makes it difficult to assess the strength of the experimental support for the interactions that form the feedback loops. The above considerations suggest that a moderate rate of false positives may be present in the dataset. The sensitivity of loop count to noise in the data therefore suggests that loop counting may not provide a good indication of the role of BQS by itself in this case. Moreover, MacIsaac et al. (2006) takes a computational approach to the construction of the GRN of S. cerevisiae using the experimental data provided by Harbison et al. (2004) as a starting point. Therefore, noisy data should be expected and caution should be used in interpreting the data. The network used was the most statistically restrictive. Although a limited number of long feedback loops is present in the dataset (E), comparison with random data indicates that this number is much smaller than expected by chance, still suggesting a selective pressure against instability. Note how the number of long feedback loops is extremely limited for the dataset derived from direct biological experimentation (A–C).DOI:http://dx.doi.org/10.7554/eLife.02863.005

Mentions: On studying feedback loops in the GRNs of these organisms (Figure 2A–C, Figure 2—figure supplement 1A,B, lightly shaded bars), we find that P. aeurginosa, S. cerevisiae and human GRNs have no feedback loops comprising three or more genes. The E. coli GRN has no feedback loops comprising four or more genes, and only two 3-gene feedback loops. M. tuberculosis has two 3-gene feedback loops and one 4-gene feedback loop. Notably, all the 3-gene feedback loops observed in real GRNs share the same peculiar structure, with implications discussed below. In contrast, when networks of the size and connectivity of the biological GRNs are constructed with randomly placed links, they display an exponential increase in feedback loops consisting of three or more genes, which number in the thousands (Figure 2A–C, Figure 2—figure supplement 1A,B, heavily shaded bars, and Figure 2—figure supplement 4B). Each of 1000 randomly simulated E. coli networks had at least one long feedback loop. The vastly different abundances of feedback loops clearly demonstrate the profound difference in topologies between real and random networks. Statistical analyses suggest that there is an extremely small probability (<10−6) that the absence of long feedback loops with >3 genes in E. coli is a chance event (Figure 2—figure supplement 2A). Similar results hold for S. cerevisiae (Figure 2—figure supplement 2B) and human (Figure 2—figure supplement 2C). These results are robust to variations in the confidence levels of the E. coli and S. cerevisiae GRNs (Figure 2—figure supplement 3E,J), despite large variations in other properties of the GRNs (Figure 2—figure supplement 3A–D,F–I), and remain valid when different random models are considered (Figure 2—figure supplement 4B).10.7554/eLife.02863.004Figure 2.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)

Feedback loops in M. tuberculosis, P. aeruginosa and other yeast datasets.Number of 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 genes, TFs and connections. For the random networks, each graph reports the mean and standard deviation. Note that Luscombe et al. (2004) was mainly concerned with the dynamics of yeast GRN and built the full GRN in such a way to better stress differences under different growth conditions. The dataset was constructed by adding to the network derived by Lee et al. (2002) new interactions derived under different experimental conditions, and the absence of a measure of confidence for the interactions makes it difficult to assess the strength of the experimental support for the interactions that form the feedback loops. The above considerations suggest that a moderate rate of false positives may be present in the dataset. The sensitivity of loop count to noise in the data therefore suggests that loop counting may not provide a good indication of the role of BQS by itself in this case. Moreover, MacIsaac et al. (2006) takes a computational approach to the construction of the GRN of S. cerevisiae using the experimental data provided by Harbison et al. (2004) as a starting point. Therefore, noisy data should be expected and caution should be used in interpreting the data. The network used was the most statistically restrictive. Although a limited number of long feedback loops is present in the dataset (E), comparison with random data indicates that this number is much smaller than expected by chance, still suggesting a selective pressure against instability. Note how the number of long feedback loops is extremely limited for the dataset derived from direct biological experimentation (A–C).DOI:http://dx.doi.org/10.7554/eLife.02863.005
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Related In: Results  -  Collection

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fig2s1: Feedback loops in M. tuberculosis, P. aeruginosa and other yeast datasets.Number of 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 genes, TFs and connections. For the random networks, each graph reports the mean and standard deviation. Note that Luscombe et al. (2004) was mainly concerned with the dynamics of yeast GRN and built the full GRN in such a way to better stress differences under different growth conditions. The dataset was constructed by adding to the network derived by Lee et al. (2002) new interactions derived under different experimental conditions, and the absence of a measure of confidence for the interactions makes it difficult to assess the strength of the experimental support for the interactions that form the feedback loops. The above considerations suggest that a moderate rate of false positives may be present in the dataset. The sensitivity of loop count to noise in the data therefore suggests that loop counting may not provide a good indication of the role of BQS by itself in this case. Moreover, MacIsaac et al. (2006) takes a computational approach to the construction of the GRN of S. cerevisiae using the experimental data provided by Harbison et al. (2004) as a starting point. Therefore, noisy data should be expected and caution should be used in interpreting the data. The network used was the most statistically restrictive. Although a limited number of long feedback loops is present in the dataset (E), comparison with random data indicates that this number is much smaller than expected by chance, still suggesting a selective pressure against instability. Note how the number of long feedback loops is extremely limited for the dataset derived from direct biological experimentation (A–C).DOI:http://dx.doi.org/10.7554/eLife.02863.005
Mentions: On studying feedback loops in the GRNs of these organisms (Figure 2A–C, Figure 2—figure supplement 1A,B, lightly shaded bars), we find that P. aeurginosa, S. cerevisiae and human GRNs have no feedback loops comprising three or more genes. The E. coli GRN has no feedback loops comprising four or more genes, and only two 3-gene feedback loops. M. tuberculosis has two 3-gene feedback loops and one 4-gene feedback loop. Notably, all the 3-gene feedback loops observed in real GRNs share the same peculiar structure, with implications discussed below. In contrast, when networks of the size and connectivity of the biological GRNs are constructed with randomly placed links, they display an exponential increase in feedback loops consisting of three or more genes, which number in the thousands (Figure 2A–C, Figure 2—figure supplement 1A,B, heavily shaded bars, and Figure 2—figure supplement 4B). Each of 1000 randomly simulated E. coli networks had at least one long feedback loop. The vastly different abundances of feedback loops clearly demonstrate the profound difference in topologies between real and random networks. Statistical analyses suggest that there is an extremely small probability (<10−6) that the absence of long feedback loops with >3 genes in E. coli is a chance event (Figure 2—figure supplement 2A). Similar results hold for S. cerevisiae (Figure 2—figure supplement 2B) and human (Figure 2—figure supplement 2C). These results are robust to variations in the confidence levels of the E. coli and S. cerevisiae GRNs (Figure 2—figure supplement 3E,J), despite large variations in other properties of the GRNs (Figure 2—figure supplement 3A–D,F–I), and remain valid when different random models are considered (Figure 2—figure supplement 4B).10.7554/eLife.02863.004Figure 2.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