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Balancing noise and plasticity in eukaryotic gene expression.

Bajić D, Poyatos JF - BMC Genomics (2012)

Bottom Line: This additionally implies that genome neighboring organization -as modifier- appears only effective in highly plastic genes.In this class, we confirm bidirectional promoters (bipromoters) as a configuration capable to reduce coupling by abating noise but also reveal an important trade-off, since bipromoters also decrease plasticity.This presents ultimately a paradox between intergenic distances and modulation, with short intergenic distances both associated and disassociated to noise at different plasticity levels.

View Article: PubMed Central - HTML - PubMed

Affiliation: Logic of Genomic Systems Laboratory, Spanish National Biotechnology Centre, Consejo Superior de Investigaciones Científicas-CSIC, Madrid, Spain. jpoyatos@cnb.csic.es

ABSTRACT

Background: Coupling the control of expression stochasticity (noise) to the ability of expression change (plasticity) can alter gene function and influence adaptation. A number of factors, such as transcription re-initiation, strong chromatin regulation or genome neighboring organization, underlie this coupling. However, these factors do not necessarily combine in equivalent ways and strengths in all genes. Can we identify then alternative architectures that modulate in distinct ways the linkage of noise and plasticity?

Results: Here we first show that strong chromatin regulation, commonly viewed as a source of coupling, can lead to plasticity without noise. The nature of this regulation is relevant too, with plastic but noiseless genes being subjected to general activators whereas plastic and noisy genes experience more specific repression. Contrarily, in genes exhibiting poor transcriptional control, it is translational efficiency what separates noise from plasticity, a pattern related to transcript length. This additionally implies that genome neighboring organization -as modifier- appears only effective in highly plastic genes. In this class, we confirm bidirectional promoters (bipromoters) as a configuration capable to reduce coupling by abating noise but also reveal an important trade-off, since bipromoters also decrease plasticity. This presents ultimately a paradox between intergenic distances and modulation, with short intergenic distances both associated and disassociated to noise at different plasticity levels.

Conclusions: Balancing the coupling among different types of expression variability appears as a potential shaping force of genome regulation and organization. This is reflected in the use of different control strategies at genes with different sets of functional constraints.

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Noise determinants in low plasticity genes. Noise in LP genes is related to translational efficiency, which in turn is related to ORF length. We ordered LP genes by increasing noise. We performed a sliding window analysis of translational efficiency (A), ribosomal density (B) and ORF length (C). Shaded regions represent the mean and two standard deviations at each point obtained with the same sliding window analysis over randomized data; the process was repeated 10000 times. See also main text.
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Figure 4: Noise determinants in low plasticity genes. Noise in LP genes is related to translational efficiency, which in turn is related to ORF length. We ordered LP genes by increasing noise. We performed a sliding window analysis of translational efficiency (A), ribosomal density (B) and ORF length (C). Shaded regions represent the mean and two standard deviations at each point obtained with the same sliding window analysis over randomized data; the process was repeated 10000 times. See also main text.

Mentions: We thus inspected if uncoupling could be associated in these genes to translation as this is known to control noise[24,25]. Our analysis shows that noise in LP genes is correlated with translational efficiency[26] and ribosomal density[27] (ρ = 0.22,p = 7.9 × 10−5, and ρ = 0.21,p = 1.6 × 10−4, respectively; n = 327, and Figure4A,B) while we did not observe this in highly plastic genes (ρ = 0.06,p = 0.32 and ρ = 0.08,p = 0.14, respectively; n = 312). If translation controls noise in LP genes, then noise should also covariate with factors influencing translation efficiency such as ORF length or codon bias (see, for instance,[27,28]). In LP genes, translational efficiency correlated more strongly with ORF length (ρ = −0.58,p = 6.7 × 10−31,n = 327) than with frequency of optimal codons (FOP, ρ = 0.31,p = 9.9 × 10−9,n = 327). Consistently, noise correlated with ORF length (ρ = −0.18,p = 5.5 × 10−5,n = 513 and Figure4C) but not so with FOP (ρ = 0.07,p = 0.12,n = 513). On the other hand, noise correlated with ORF length in an opposite way in HP genes (ρ = 0.20,p = 5.6 × 10−6,n = 309) which probably reflects complementary constraints on gene length (e.g., low noise genes in the HP class are mostly ribosomal genes, see Additional file1: Figure S9. These genes may exhibit short length due to minimization of biosynthetic costs given their high expression[29]).


Balancing noise and plasticity in eukaryotic gene expression.

Bajić D, Poyatos JF - BMC Genomics (2012)

Noise determinants in low plasticity genes. Noise in LP genes is related to translational efficiency, which in turn is related to ORF length. We ordered LP genes by increasing noise. We performed a sliding window analysis of translational efficiency (A), ribosomal density (B) and ORF length (C). Shaded regions represent the mean and two standard deviations at each point obtained with the same sliding window analysis over randomized data; the process was repeated 10000 times. See also main text.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3539894&req=5

Figure 4: Noise determinants in low plasticity genes. Noise in LP genes is related to translational efficiency, which in turn is related to ORF length. We ordered LP genes by increasing noise. We performed a sliding window analysis of translational efficiency (A), ribosomal density (B) and ORF length (C). Shaded regions represent the mean and two standard deviations at each point obtained with the same sliding window analysis over randomized data; the process was repeated 10000 times. See also main text.
Mentions: We thus inspected if uncoupling could be associated in these genes to translation as this is known to control noise[24,25]. Our analysis shows that noise in LP genes is correlated with translational efficiency[26] and ribosomal density[27] (ρ = 0.22,p = 7.9 × 10−5, and ρ = 0.21,p = 1.6 × 10−4, respectively; n = 327, and Figure4A,B) while we did not observe this in highly plastic genes (ρ = 0.06,p = 0.32 and ρ = 0.08,p = 0.14, respectively; n = 312). If translation controls noise in LP genes, then noise should also covariate with factors influencing translation efficiency such as ORF length or codon bias (see, for instance,[27,28]). In LP genes, translational efficiency correlated more strongly with ORF length (ρ = −0.58,p = 6.7 × 10−31,n = 327) than with frequency of optimal codons (FOP, ρ = 0.31,p = 9.9 × 10−9,n = 327). Consistently, noise correlated with ORF length (ρ = −0.18,p = 5.5 × 10−5,n = 513 and Figure4C) but not so with FOP (ρ = 0.07,p = 0.12,n = 513). On the other hand, noise correlated with ORF length in an opposite way in HP genes (ρ = 0.20,p = 5.6 × 10−6,n = 309) which probably reflects complementary constraints on gene length (e.g., low noise genes in the HP class are mostly ribosomal genes, see Additional file1: Figure S9. These genes may exhibit short length due to minimization of biosynthetic costs given their high expression[29]).

Bottom Line: This additionally implies that genome neighboring organization -as modifier- appears only effective in highly plastic genes.In this class, we confirm bidirectional promoters (bipromoters) as a configuration capable to reduce coupling by abating noise but also reveal an important trade-off, since bipromoters also decrease plasticity.This presents ultimately a paradox between intergenic distances and modulation, with short intergenic distances both associated and disassociated to noise at different plasticity levels.

View Article: PubMed Central - HTML - PubMed

Affiliation: Logic of Genomic Systems Laboratory, Spanish National Biotechnology Centre, Consejo Superior de Investigaciones Científicas-CSIC, Madrid, Spain. jpoyatos@cnb.csic.es

ABSTRACT

Background: Coupling the control of expression stochasticity (noise) to the ability of expression change (plasticity) can alter gene function and influence adaptation. A number of factors, such as transcription re-initiation, strong chromatin regulation or genome neighboring organization, underlie this coupling. However, these factors do not necessarily combine in equivalent ways and strengths in all genes. Can we identify then alternative architectures that modulate in distinct ways the linkage of noise and plasticity?

Results: Here we first show that strong chromatin regulation, commonly viewed as a source of coupling, can lead to plasticity without noise. The nature of this regulation is relevant too, with plastic but noiseless genes being subjected to general activators whereas plastic and noisy genes experience more specific repression. Contrarily, in genes exhibiting poor transcriptional control, it is translational efficiency what separates noise from plasticity, a pattern related to transcript length. This additionally implies that genome neighboring organization -as modifier- appears only effective in highly plastic genes. In this class, we confirm bidirectional promoters (bipromoters) as a configuration capable to reduce coupling by abating noise but also reveal an important trade-off, since bipromoters also decrease plasticity. This presents ultimately a paradox between intergenic distances and modulation, with short intergenic distances both associated and disassociated to noise at different plasticity levels.

Conclusions: Balancing the coupling among different types of expression variability appears as a potential shaping force of genome regulation and organization. This is reflected in the use of different control strategies at genes with different sets of functional constraints.

Show MeSH