Versatility of Cooperative Transcriptional Activation: A Thermodynamical Modeling Analysis for Greater-Than-Additive and Less-Than-Additive Effects

Abstract

We derive a statistical model of transcriptional activation using equilibrium thermodynamics of chemical reactions. We examine to what extent this statistical model predicts synergy effects of cooperative activation of gene expression. We determine parameter domains in which greater-than-additive and less-than-additive effects are predicted for cooperative regulation by two activators. We show that the statistical approach can be used to identify different causes of synergistic greater-than-additive effects: nonlinearities of the thermostatistical transcriptional machinery and three-body interactions between RNA polymerase and two activators. In particular, our model-based analysis suggests that at low transcription factor concentrations cooperative activation cannot yield synergistic greater-than-additive effects, i.e., DNA transcription can only exhibit less-than-additive effects. Accordingly, transcriptional activity turns from synergistic greater-than-additive responses at relatively high transcription factor concentrations into less-than-additive responses at relatively low concentrations. In addition, two types of re-entrant phenomena are predicted. First, our analysis predicts that under particular circumstances transcriptional activity will feature a sequence of less-than-additive, greater-than-additive, and eventually less-than-additive effects when for fixed activator concentrations the regulatory impact of activators on the binding of RNA polymerase to the promoter increases from weak, to moderate, to strong. Second, for appropriate promoter conditions when activator concentrations are increased then the aforementioned re-entrant sequence of less-than-additive, greater-than-additive, and less-than-additive effects is predicted as well. Finally, our model-based analysis suggests that even for weak activators that individually induce only negligible increases in promoter activity, promoter activity can exhibit greater-than-additive responses when transcription factors and RNA polymerase interact by means of three-body interactions. Overall, we show that versatility of transcriptional activation is brought about by nonlinearities of transcriptional response functions and interactions between transcription factors, RNA polymerase and DNA.

Effective Single-Step Posttranscriptional Dynamics Allowing for a Direct Maximum Likelihood Estimation of Transcriptional Activity and the Quantification of Sources of Gene Expression Variability with an Illustration for the Hypoxia and TNFαRegulated Inflammatory Pathway

“…The single stimulation conditions produced transcriptional activity levels in the medium range of = 0.30 (normoxia with 1 ng/mL TNF ) and = 0.30 again (hypoxia with 0 ng/mL TNF ). In particular, under dual stimulation the activity was higher ( = 0.87) than the sum of the activity levels observed under single stimulation ( = 0.60), which indicates that the COX2 promoter activity was synergistically regulated [36] and is consistent with the results obtained by Bruning et al, 2012 [1] using linear regression analysis. Two-way ANOVA analysis was conducted to test the statistical significance of the synergy effect [37].…”

“…To this end, the transcriptional activity parameter is considered as a function of the transcription factor concentrations. Explicit models, for example, using a thermostatistical approach have been proposed for that purpose (see [4,36] and references therein). Unknown parameters of such thermostatistical models or functional dependencies between and transcription factor concentrations can be estimated when (in replacement for ) is estimated for dose responses of the set of transcription factors of interest.…”

“…Consequently, they are statistically independent (see, e.g., [35]). From (36) it follows that the residuals defined by…”

Advances in dynamic modeling of colorectal cancer signaling-network regions, a path toward targeted therapies

“…A single (as a first-approximation) transcription rate function for the MYC and CCND1 genes was mathematically derived using a statistical thermodynamic framework [71-73]. First, the key transcription factor binding sites (TFBSs) responsible for activation or repression of MYC and CCND1 were identified, as well as the main transcription factors (TFs) that bind to them (Fig.…”

Three-factor models versus time series models: quantifying time-dependencies of interactions between stimuli in cell biology and psychobiology for short longitudinal data

“…In this context, cooperative activation means that if a gene is activated cooperatively by two extra-cellular stimuli A and B, then the expression level of that gene induced by the combined stimulation with A and B is higher than the expected level obtained by summing up the expression levels induced by the individual stimulations with either A or B. Such greater-than-additive effects have been referred to as 'synergy' effects (Ptashne & Gann, 1997;Levine, 2010;Frank et al, 2012a). In what follows, we will refer to them as interaction effects in order to use a more technical term.…”