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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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Related in: MedlinePlus

3- and 4-gene motifs in P. aeruginosa, M. tuberculosis and other yeast datasets.Number of selected 3- and 4-gene motifs for M. tuberculosis (A and B), P. aeruginosa (C and D), the yeast dataset derived from Lee et al. (2002) (E and F), the yeast dataset derived from Luscombe et al. (2004) (G and H), and the yeast dataset derived from MacIsaac et al. (2006) (I and J). Note how the predictions of BQS are verified in all the datasets.DOI:http://dx.doi.org/10.7554/eLife.02863.022
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fig5s2: 3- and 4-gene motifs in P. aeruginosa, M. tuberculosis and other yeast datasets.Number of selected 3- and 4-gene motifs for M. tuberculosis (A and B), P. aeruginosa (C and D), the yeast dataset derived from Lee et al. (2002) (E and F), the yeast dataset derived from Luscombe et al. (2004) (G and H), and the yeast dataset derived from MacIsaac et al. (2006) (I and J). Note how the predictions of BQS are verified in all the datasets.DOI:http://dx.doi.org/10.7554/eLife.02863.022

Mentions: Our discussion so far has focused on the effect of BQS on large- and intermediate-scale global properties of GRNs. BQS also makes strong predictions about the small-scale local structure of GRNs. To investigate this, we dissected each of the GRNs into a series of small motifs comprising three or four genes (Alon, 2006; Milo et al., 2002). A single motif can, in principle, break Qualitative Stability by forming a feedback loop composed of three or more genes. As we have shown above, such motifs are essentially absent from real GRNs. However, motifs may be susceptible to feedback loop formation through the addition of a link, and we can therefore speak of ‘buffered motifs’ as motifs that are resilient to this, and therefore enhance BQS locally. Note that, to prevent possible biases introduced by the large number of non-TF genes, only motifs completely formed by TFs were considered. Using symmetry arguments, we grouped 3- and 4-gene motifs into buffered and non-buffered categories, which are equi-probable in a random network (confirmed by Figure 5—figure supplement 4B,E,F,H,K,L). Figure 5A–F show that in the real GRNs of E. coli, S. cerevisiae and human, buffered motifs (blue) are much more abundant than would be expected by chance, while unbuffered (green and violet) motifs are much less abundant; and indeed, unbuffered motifs which are particularly susceptible to breaking BQS (violet) are rare. Similar results hold for other confidence levels of E. coli (Figure 5—figure supplement 3A–L), other confidence levels of S. cerevisiae (Figure 5—figure supplement 3M–X), M. tuberculosis (Figure 5—figure supplement 2A,B), P. aeruginosa (Figure 5—figure supplement 2,D), and other yeast datasets (Figure 5—figure supplement 2E–J). Note that the IDs used in Figure 5—figure supplements 2–4 are described by Figure 5—figure supplement 1.10.7554/eLife.02863.020Figure 5.BQS in selected 3- and 4-gene motifs.


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

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

3- and 4-gene motifs in P. aeruginosa, M. tuberculosis and other yeast datasets.Number of selected 3- and 4-gene motifs for M. tuberculosis (A and B), P. aeruginosa (C and D), the yeast dataset derived from Lee et al. (2002) (E and F), the yeast dataset derived from Luscombe et al. (2004) (G and H), and the yeast dataset derived from MacIsaac et al. (2006) (I and J). Note how the predictions of BQS are verified in all the datasets.DOI:http://dx.doi.org/10.7554/eLife.02863.022
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4151086&req=5

fig5s2: 3- and 4-gene motifs in P. aeruginosa, M. tuberculosis and other yeast datasets.Number of selected 3- and 4-gene motifs for M. tuberculosis (A and B), P. aeruginosa (C and D), the yeast dataset derived from Lee et al. (2002) (E and F), the yeast dataset derived from Luscombe et al. (2004) (G and H), and the yeast dataset derived from MacIsaac et al. (2006) (I and J). Note how the predictions of BQS are verified in all the datasets.DOI:http://dx.doi.org/10.7554/eLife.02863.022
Mentions: Our discussion so far has focused on the effect of BQS on large- and intermediate-scale global properties of GRNs. BQS also makes strong predictions about the small-scale local structure of GRNs. To investigate this, we dissected each of the GRNs into a series of small motifs comprising three or four genes (Alon, 2006; Milo et al., 2002). A single motif can, in principle, break Qualitative Stability by forming a feedback loop composed of three or more genes. As we have shown above, such motifs are essentially absent from real GRNs. However, motifs may be susceptible to feedback loop formation through the addition of a link, and we can therefore speak of ‘buffered motifs’ as motifs that are resilient to this, and therefore enhance BQS locally. Note that, to prevent possible biases introduced by the large number of non-TF genes, only motifs completely formed by TFs were considered. Using symmetry arguments, we grouped 3- and 4-gene motifs into buffered and non-buffered categories, which are equi-probable in a random network (confirmed by Figure 5—figure supplement 4B,E,F,H,K,L). Figure 5A–F show that in the real GRNs of E. coli, S. cerevisiae and human, buffered motifs (blue) are much more abundant than would be expected by chance, while unbuffered (green and violet) motifs are much less abundant; and indeed, unbuffered motifs which are particularly susceptible to breaking BQS (violet) are rare. Similar results hold for other confidence levels of E. coli (Figure 5—figure supplement 3A–L), other confidence levels of S. cerevisiae (Figure 5—figure supplement 3M–X), M. tuberculosis (Figure 5—figure supplement 2A,B), P. aeruginosa (Figure 5—figure supplement 2,D), and other yeast datasets (Figure 5—figure supplement 2E–J). Note that the IDs used in Figure 5—figure supplements 2–4 are described by Figure 5—figure supplement 1.10.7554/eLife.02863.020Figure 5.BQS in selected 3- and 4-gene motifs.

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