Gertz, J., Siggia, E. D. & Cohen, B. A. Analysis of combinatorial cis-regulation in synthetic and genomic promoters. Nature 457, 215-218 (2008). URLhttp://dx.doi.org/10.1038/nature07521.
- a technical note
the expression level was measured by fluorescence to cell volume ratio for 25,000 cells in flowcytometry and take the mean. The reason to use the ratio is that the number of transcripts, i.e. expression level, depends on not only the promoter strength but also the cell’s “developmental stage”. Naively, one would expect that a cell grows its size and linearly increases its transcript number. Therefore the logic behind this ratio measure. - 1st result — Mig1 binds cooperatively
Hill equation with a Hill coefficient of 3.4 and K 5 1.8 (red) fits the observed data (blue) well, compared to a Hill equation with a Hill coefficient of 1 (green).
- Role of weak binding sites
It’s a bit different than what I used to understand. Here is my current (I think correct) understanding: weak sites, when nearby a strong site, doesn’t get promoted to be a strong site, at least according to the model. This is what I used to believe what they suggest. But in fact, they estimated a free parameter that measures the ratio of affinity of MIG1 protein to the weak site to its affinity to a strong site, which gives 6.7. They claim that this is in the 95% CI of the PWM prediction, which suggests 9 fold lower affinity for the weaker site. The reason they suggest a weak site is useful is through its cooperative binding with the strong site. ( how is this the case? ) See figure below:
Plots of expression for pairs of promoters that are almost identical except that either one strong Mig1 site or two strong Mig1 sites replace one strong and one weak Mig1 site. A blue circle represents one promoter pair and the red line represents equal expression.
- Role of weak binding sites in vivo
They have two strategies to link their findings from the synthetic libraries to the in vivo biology
(1) compare the number of co-occurrence of weak site in promoters that harbor a strong site with that of shuffled promoters
(2) they did a slightly trickier comparison: they found 359 promoters that contain at least one strong Mig1 site from all yeast promoters. They then applied their thermodynamic model through all the promoters, rank them by the predicted Mig1 repression and take the top ranking 359. As a test, they had 136 documented Mig1 target genes (perhaps by some microarray assays). They use this as a reference set and ask how much could the two 359 promoters sets overlap with the 136 biologically defined targets. In the end, their thermodynamic model predicted set predicts 8 more promoters out of the 136 (41 vs 33). This demonstrates that the thermodynamic model captures more features of repression than a simple look for strong hit.
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