The kinetic landscape of human transcription factors

Abstract

Cell-to-cell variability is shaped by transcription dynamics because genes are transcribed in bursts interspersed with inactive periods. The stochasticity of bursting means that genes transcribed in rare bursts exhibit more heterogeneity at the single cell level than genes that burst often1,2. Transcription starts with the binding of Transcription Factors (TFs) to specific sequence motifs where they recruit the transcription machinery3. In some systems, individual TF binding events temporally correlate with the firing of transcriptional bursts, defining the target gene's frequency and duration4-6. However, in the absence of methods that assess the impact of different TFs on transcription dynamics at the same genetic loci, it remains unclear whether DNA binding kinetics are the sole determinant of bursting. Here we develop an imaging-based synthetic recruitment assay, CRISPRburst, and measure how 92 human TFs impact bursting kinetics. We show that TFs recruited to chromatin under identical conditions generate diverse bursting signatures, some TFs increasing the probability of the gene turning on while others increase the number of mRNA molecules transcribed per burst. We find that the association of TFs with specific protein partners determines their bursting output, and train a model to predict the kinetic signatures of all human TFs. These kinetic signatures can be used as a TF classification system complementary to existing families based on DNA binding domains. Additionally, kinetic signatures provide a rational framework to design synthetic activators, model transcription regulation, and understand expression heterogeneity.

Time will tell: comparing timescales to gain insight into transcriptional bursting

“…These bursts are characterized by three parameters: the rate of switching ON, the Pol II initiation rate during the ON state, and the rate of switching OFF [ 6 ]. Gene activity can be modulated by changing any of these three rates [ 3 , 7 , 8 ], and quantification of the bursting parameters with live-cell transcription imaging provides insight into which kinetic steps are regulated by different regulatory mechanisms. As the experimental detection of the bursts has guided its descriptive terminology, we shortly discuss variations in nomenclature ( Box 1 ) to avoid confusion while assessing the literature.…”

“…Mathematically, one can then define a more complex multistate model with multiple OFF states with additional rates to describe these bursting kinetics. The ON and OFF times together determine the active fraction or ON probability [ 8 , 13 ]. This parameter can also be determined using snapshot approaches, such as smFISH, by determining the number of actively transcribing alleles.…”

“…Recent insights suggest substantial variations in the dynamic range of bursting parameters, with burst frequency exhibiting much larger differences between conditions than burst duration or amplitude [ 3 , 8 , 9 ], suggesting that burst frequency is likely the main regulatory ‘knob’ to control the total RNA output. This finding is in line with a study combining Pol II chromatin immunoprecipitation with single-cell transcription measurements, which showed that burst frequency and pause release, rather than Pol II loading, are key rate-limiting steps for transcriptional regulation [ 10 ].…”

“…], implying that regulatory mechanisms driving the ON–OFF switching are interdependent. Although these data suggest that bursting parameters are somehow constrained, there is also ample evidence for independent control of each parameter [ 7 , 8 , 14 ]. Nevertheless, the most extreme combinations of bursting parameters (e.g., large but infrequent bursts) likely do not naturally occur inside cells.…”

“…Clusters of p300 thereby support a faster initiation rate and an increased ON time [ 78 , 79 ]. Conversely, for the Bmal1 circadian promoter and an HIV reporter, p300 recruitment influences mostly the OFF time [ 8 , 80 ]. These differences may come from the different activating mechanisms of p300, where the effects on TF recruitment, chromatin phase separation, BRD4 recruitment, and Pol II pause release depend on histone acetylation, but p300 clustering itself can also facilitate TF binding independent of acetylation [ 78 ].…”