The ability of cells to accurately control gene expression levels in response to extracellular cues is limited by the inherently stochastic nature of transcriptional regulation. A change in transcription factor (TF) activity results in changes in the expression of its targets, but the way in which cell-to-cell variability in expression (noise) changes as a function of TF activity, and whether targets of the same TF behave similarly, is not known. Here, we measure expression and noise as a function of TF activity for 16 native targets of the transcription factor Zap1 that are regulated by it through diverse mechanisms. For most activated and repressed Zap1 targets, noise decreases as expression increases. Kinetic modeling suggests that this is due to two distinct Zap1-mediated mechanisms that both change the frequency of transcriptional bursts. Notably, we found that another mechanism of repression by Zap1, which is encoded in the promoter DNA, likely decreases the size of transcriptional bursts, producing a unique transcriptional state characterized by low expression and low noise. In addition, we find that further reduction in noise is achieved when a single TF both activates and represses a single target gene. Our results suggest a global principle whereby at low TF concentrations, the dominant source of differences in expression between promoters stems from differences in burst frequency, whereas at high TF concentrations differences in burst size dominate. Taken together, we show that the precise amount by which noise changes with expression is specific to the regulatory mechanism of transcription and translation that acts at each gene.

This work was supported by grants from the European Research Council (ERC) and The US National Institutes of Health (NIH) to ES. ES is the incumbent of the Soretta and Henry Shapiro Career Development Chair. PMAS and DD were supported by FP7 FET Open project ‘DynaNets’ (EU Grant Agreement no. 233847), and ‘ViroLab’ (EU project no: IST-027446). PMAS acknowledges the Complexity Program at NTU, Singapore and the financial support from the FET-Proactive grant TOPDRIM, number FP7-ICT-318121. DD was also supported by an EMBO Short-Term Fellowship and a travel grant from the BioRange program of the Netherlands Bioinformatics Centre (NBIC) and Netherlands Genomics Initiative (NGI). JK was supported by the FP7 collaborative project BioPreDyn (contract no. 289434). LBC was supported by a Weizmann Institute postdoctoral fellowship