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Incorporating Nucleosomes into Thermodynamic Models of Transcription Regulation

Tali Raveh-Sadka1*, Michal Levo1*, Eran Segal1,2,†

Transcriptional control is central to many cellular processes and consequently, much effort has been devoted to understanding its underlying mechanisms. The organization of nucleosomes along promoter regions is important for this process, since most transcription factors cannot bind nucleosomal sequences, and thus compete with nucleosomes for DNA access. This competition is governed by the relative concentrations of nucleosomes and transcription factors and by their respective sequence binding preferences. However, despite its importance, a mechanistic understanding of the quantitative effects that the competition between nucleosomes and factors has on transcription is still missing. Here we employ a thermodynamic framework based on fundamental principles of statistical mechanics to theoretically explore the effect that different nucleosome organizations along promoters have on the activation dynamics of promoters in response to varying concentrations of the regulating factors. We show that even simple landscapes of nucleosome organization reproduce experimental results regarding the effect of nucleosomes as general repressors and as generators of obligate binding cooperativity between factors. Our modeling framework also allows us to characterize the effects that various sequence elements of promoters have on the induction threshold and on the shape of the promoter activation curves. Finally, we show that using only sequence preferences for nucleosomes and transcription factors, our model can also predict expression of real promoter sequences, thereby underscoring the importance of the interplay between nucleosomes and factors in determining expression kinetics.
This paper is accompanied by supplementary information.


Illustration of our thermodynamic framework. Each promoter sequence encodes particular binding affinity landscapes for both transcription factors and nucleosomes. Given these landscapes as input as well as the concentrations of transcription factors and nucleosomes, our framework can then compute the distribution over all possible configurations of molecules bound to the promoter (see Modeling Framework). Applying these computations to different promoters (represented by different affinity landscapes) and over a range of transcription factor concentrations thus allows us to compute the activation curve of various promoters as a function of transcription factor concentrations.


* These authors contributed equally to this work.
Correspondence should be addressed to E.S.
1 Dept. of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel.
2 Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, 76100, Israel.