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Effects of post-transcriptional regulation on phenotypic noise in Escherichia coli.

Arbel-Goren R, Tal A, Friedlander T, Meshner S, Costantino N, Court DL, Stavans J - Nucleic Acids Res. (2013)

Bottom Line: Cell-to-cell variations in protein abundance, called noise, give rise to phenotypic variability between isogenic cells.Studies of noise have focused on stochasticity introduced at transcription, yet the effects of post-transcriptional regulatory processes on noise remain unknown.Extrinsic noise provides the dominant contribution to the total protein noise over the total range of RyhB production rates.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel.

ABSTRACT
Cell-to-cell variations in protein abundance, called noise, give rise to phenotypic variability between isogenic cells. Studies of noise have focused on stochasticity introduced at transcription, yet the effects of post-transcriptional regulatory processes on noise remain unknown. We study the effects of RyhB, a small-RNA of Escherichia coli produced on iron stress, on the phenotypic variability of two of its downregulated target proteins, using dual chromosomal fusions to fluorescent reporters and measurements in live individual cells. The total noise of each of the target proteins is remarkably constant over a wide range of RyhB production rates despite cells being in stress. In fact, coordinate downregulation of the two target proteins by RyhB reduces the correlation between their levels. Hence, an increase in phenotypic variability under stress is achieved by decoupling the expression of different target proteins in the same cell, rather than by an increase in the total noise of each. Extrinsic noise provides the dominant contribution to the total protein noise over the total range of RyhB production rates. Stochastic simulations reproduce qualitatively key features of our observations and show that a feed-forward loop formed by transcriptional extrinsic noise, an sRNA and its target genes exhibits strong noise filtration capabilities.

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Effects of iron deprivation on the statistics of protein concentration distributions of RyhB targets. Standard deviation  as a function of mean protein concentration  of SodB-CFP (full circles) and FumA-YFP (empty circles), for different production of RyhB induced by iron deprivation. Lines represent linear fits to the data (R2 = 0.957 and R2 = 0.877, respectively). Error bars were calculated using 1000 bootstrap samplings of the data. Cells were exposed to different concentrations of DTPA in four independent experiments, each carried out under the same conditions as in Figure 1. The data were corrected for cell auto-fluorescence.
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gkt184-F3: Effects of iron deprivation on the statistics of protein concentration distributions of RyhB targets. Standard deviation as a function of mean protein concentration of SodB-CFP (full circles) and FumA-YFP (empty circles), for different production of RyhB induced by iron deprivation. Lines represent linear fits to the data (R2 = 0.957 and R2 = 0.877, respectively). Error bars were calculated using 1000 bootstrap samplings of the data. Cells were exposed to different concentrations of DTPA in four independent experiments, each carried out under the same conditions as in Figure 1. The data were corrected for cell auto-fluorescence.

Mentions: To analyze cell-to-cell variability, we calculated the mean and standard deviation from the data. In most studies of phenotypic variability, noise is quantified by the ratio . This quantity exhibits large fluctuations for small , i.e. when fluorescence intensities are close to the background, which occurs when iron levels are very low. To avoid dividing by small numbers, we plotted as a function of . We plot as a function of for distributions of SodB-CFP and FumA-YFP concentrations measured under different levels of iron deprivation in Figure 3. There are two salient features in the data. First, the dependence of on is approximately linear in both cases, implying that the noise ratio should be rather insensitive to the level of iron deprivation (Supplementary Figure S7). Second, linear fits to the data of both proteins coincide within experimental error: both the slopes (0.33 ± 0.02 for SodB-CFP and 0.31 ± 0.04 for FumA-YFP), as well as their intercept on the axis ( nM for both proteins, equivalent to approximately seven copies of each protein per cell), are essentially the same (Supplementary Text). Together, these features suggest a common gene-independent mechanism governing the protein phenotypic variability of the two RyhB target genes, as iron deprivation is changed (Supplementary Text).Figure 3.


Effects of post-transcriptional regulation on phenotypic noise in Escherichia coli.

Arbel-Goren R, Tal A, Friedlander T, Meshner S, Costantino N, Court DL, Stavans J - Nucleic Acids Res. (2013)

Effects of iron deprivation on the statistics of protein concentration distributions of RyhB targets. Standard deviation  as a function of mean protein concentration  of SodB-CFP (full circles) and FumA-YFP (empty circles), for different production of RyhB induced by iron deprivation. Lines represent linear fits to the data (R2 = 0.957 and R2 = 0.877, respectively). Error bars were calculated using 1000 bootstrap samplings of the data. Cells were exposed to different concentrations of DTPA in four independent experiments, each carried out under the same conditions as in Figure 1. The data were corrected for cell auto-fluorescence.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3643596&req=5

gkt184-F3: Effects of iron deprivation on the statistics of protein concentration distributions of RyhB targets. Standard deviation as a function of mean protein concentration of SodB-CFP (full circles) and FumA-YFP (empty circles), for different production of RyhB induced by iron deprivation. Lines represent linear fits to the data (R2 = 0.957 and R2 = 0.877, respectively). Error bars were calculated using 1000 bootstrap samplings of the data. Cells were exposed to different concentrations of DTPA in four independent experiments, each carried out under the same conditions as in Figure 1. The data were corrected for cell auto-fluorescence.
Mentions: To analyze cell-to-cell variability, we calculated the mean and standard deviation from the data. In most studies of phenotypic variability, noise is quantified by the ratio . This quantity exhibits large fluctuations for small , i.e. when fluorescence intensities are close to the background, which occurs when iron levels are very low. To avoid dividing by small numbers, we plotted as a function of . We plot as a function of for distributions of SodB-CFP and FumA-YFP concentrations measured under different levels of iron deprivation in Figure 3. There are two salient features in the data. First, the dependence of on is approximately linear in both cases, implying that the noise ratio should be rather insensitive to the level of iron deprivation (Supplementary Figure S7). Second, linear fits to the data of both proteins coincide within experimental error: both the slopes (0.33 ± 0.02 for SodB-CFP and 0.31 ± 0.04 for FumA-YFP), as well as their intercept on the axis ( nM for both proteins, equivalent to approximately seven copies of each protein per cell), are essentially the same (Supplementary Text). Together, these features suggest a common gene-independent mechanism governing the protein phenotypic variability of the two RyhB target genes, as iron deprivation is changed (Supplementary Text).Figure 3.

Bottom Line: Cell-to-cell variations in protein abundance, called noise, give rise to phenotypic variability between isogenic cells.Studies of noise have focused on stochasticity introduced at transcription, yet the effects of post-transcriptional regulatory processes on noise remain unknown.Extrinsic noise provides the dominant contribution to the total protein noise over the total range of RyhB production rates.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel.

ABSTRACT
Cell-to-cell variations in protein abundance, called noise, give rise to phenotypic variability between isogenic cells. Studies of noise have focused on stochasticity introduced at transcription, yet the effects of post-transcriptional regulatory processes on noise remain unknown. We study the effects of RyhB, a small-RNA of Escherichia coli produced on iron stress, on the phenotypic variability of two of its downregulated target proteins, using dual chromosomal fusions to fluorescent reporters and measurements in live individual cells. The total noise of each of the target proteins is remarkably constant over a wide range of RyhB production rates despite cells being in stress. In fact, coordinate downregulation of the two target proteins by RyhB reduces the correlation between their levels. Hence, an increase in phenotypic variability under stress is achieved by decoupling the expression of different target proteins in the same cell, rather than by an increase in the total noise of each. Extrinsic noise provides the dominant contribution to the total protein noise over the total range of RyhB production rates. Stochastic simulations reproduce qualitatively key features of our observations and show that a feed-forward loop formed by transcriptional extrinsic noise, an sRNA and its target genes exhibits strong noise filtration capabilities.

Show MeSH
Related in: MedlinePlus