An intrinsically disordered region-mediated confinement state contributes to the dynamics and function of transcription factors

Abstract

Transcription factors (TFs) regulate gene expression by binding to specific consensus motifs within the local chromatin context. The mechanisms by which TFs navigate the nuclear environment as they search for binding sites remain unclear. Here, we used single-molecule tracking and machine-learning-based classification to directly measure the nuclear mobility of the glucocorticoid receptor (GR) in live cells. We revealed two distinct and dynamic low-mobility populations. One accounts for specific binding to chromatin, while the other represents a confinement state that requires an intrinsically disordered region (IDR), implicated in liquid-liquid condensate subdomains. Further analysis showed that the dwell times of both subpopulations follow a power-law distribution, consistent with a broad distribution of affinities on the GR cistrome and interactome. Together, our data link IDRs with a confinement state that is functionally distinct from specific chromatin binding and modulates the transcriptional output by increasing the local concentration of TFs at specific sites.

Keywords: GRdim; GRmon; PPAR; bi-exponential; chromatin binding; confinement; glucocorticoid receptor; intrinsically disordered regions; power-law; single-molecule; transcription dynamics.

Figure 1.. pEM-based MSD analysis reveals four types of GR movement within the nucleus

(A) Representative temporal projection image of an SMT experiment via HiLO imaging (top) with superimposed particle trajectories sampled over 84 ms with continuous acquisition (12 ms exposure, GRwt-Halo) (bottom). (B) Representative examples of particle trajectories of the observed populations classified by pEM. (C) MSD versus lag time for the four families of trajectories exhibited by GRwt-Halo conjugated with PA-JF₅₄₉ and treated with 100 nM Dex (15–120 min prior to imaging). The right panel shows a zoomed-in section of the same plot. The noise floor was calculated by imaging GRwt-Halo in fixed cells (GR-fixed, black dotted line). MSDs are calculated from 7-frame tracks. The number of cells/tracks is 109/33,377. Error bars denote standard error measure (SEM). See also Figure S1.

Figure 2.. Chromatin binding accounts for one of the GR low-mobility states

(A) Randomly selected particle trajectories of the two low-mobility states of GRwt-Halo conjugated with JF₅₄₉ and treated with 100 nM Dex (15–120 min prior to imaging) found by pEM analysis of 7-frame track segments, with a 200 ms acquisition interval, 10 ms exposure. (B) MSD versus lag time of GRwt-Halo (solid lines, #cells/#tracks are 70/21,535) and GR-C428G (dashed lines, #cells/#tracks are 52/20,354). MSDs are calculated from 7-frame track segments, with a 200 ms acquisition interval, 10 ms exposure. (C) Schematic of GR structural domains and location of the C428G mutation (arrow). (D) Heatmap representation of ChIP-seq from the indicated cell lines, +/− 100 nM Dex for 1 h. Binding intensity is noted below on a linear scale. Heatmaps are sorted based on GRwt binding intensity and normalized for read depth and local tag density. (E) MSD versus lag time as described in (B) with Dex-treated GRwt-Halo (solid lines, #cells/#tracks are 70/21,535) and 4 h Cort washout (dashed lines, #cells/#tracks are 60/32593). (F) Representative projection image of Halo-GRwt. GFP-NF1 serves as a marker for the tandem array. ROI, region of interest. Scale bar 5 μm. (G) MSD versus lag time as described in (B) for the nucleoplasm (#cells/#tracks are 82/7689) or the array (#cells/#tracks are 82/1866), with a 252 ms acquisition interval, 10 ms exposure. (H) Proportions of two low-mobility states from (G) showing the relative fractions of tracks obtained from the nucleoplasm versus the array. (I) Weighted MSD versus lag time for GRwt-Dex (solid lines, #cells/#tracks are 70/21,535), HaloTag-alone (blue dashed line, #cells/#tracks are 64/16,819), and GR vehicle (black dashed line, #cells/#tracks are 47/6236). The noise floor was calculated as in Figure 1C. In all cases, error bars denote SEM. See also Figure S2.

Figure 3.. Interactions mediated by IDRs lead to confined diffusion of single TF molecules

(A and G) Plot of inherent protein disorder probability due to a lack of intrachain interactions as predicted by IUPred2A (blue) and ANCHOR (red) models for GR (A) and PPARα (G). The y axis denotes probability (0–1), and the x axis denotes amino acid position. Regions that have a score exceeding 0.5 (dashed line) are classified as disordered regions. (B and H) MSD versus lag times of GRwt-Halo (solid lines, #cells/#tracks are 70/21,535), GR-407C (B, #cells/#tracks are 60/37,662), or PPARα (H, #cells/#tracks are 60/12,237, respectively) treated with 100 nM Dex or 10 μM WY-14643 (15–120 min prior to imaging). The plot shows MSD of 7-frame tracks, 200-ms acquisition interval, and 10 ms exposure. Error bars denote SEM. (C) ChIP-seq heatmaps (top) and aggregate plots (bottom) of GFP-tagged GRwt and GR407C stably expressed in GRKO cells, +/− 100 nM Dex for 1 h. Heatmaps are sorted by GRwt binding intensity and clustered by GRwt-specific peaks and GRwt/GR407C-shared peaks, noted on the left. Heatmap binding intensity is noted to the right on a linear scale. (D) ATAC-seq heatmap (left) and the same +Dex ChIP-seq data as shown in (C) (right) re-sorted within each cluster by No-Dex ATAC signal intensity. ATAC signal intensity is noted at the left of the heatmap on a linear scale. (E) Motif analyses of each GR binding cluster at shared sites (blue) as compared to GRwt-specific sites (green). The position weight matrix (PWM) of the motifs is shown below. (F) Distribution of log-odds of a GRE motif at shared sites (blue) and GRwt-specific sites (green). CDF, cumulative distribution function. The x axis represents bins of log-odds. Comparisons using the two sample Kolmogorov-Smirnov test (p < 0.01) are shown. See also Figure S3 and Table S1.

Figure 4.. Histones exhibit both confined and chromatin-bound populations

(A and B) MSD versus lag time of GRwt-Halo treated with 100 nM Dex (15–120 min prior to imaging) (solid lines, #cells/#tracks are 70/21,535) and untreated HaloTag-H2B (dashed lines, #cells/#tracks are 70/27,218, respectively). Plots were obtained from 7-frame (A) or 30-frame (B) track segmentation, 200 ms acquisition interval, and 10 ms exposure. Error bars denote SEM. (C) Pie charts showing percentage of the different diffusive states for GRwt (Dex), GRwt (Cort), and H2B. For 12 ms acquisition, #cells/#tracks are 100/20,000 for H2B, 109/33,377 for GRwt-Dex, and 101/22,182 for GRwt-Cort. For 200 ms acquisition, #cells/#tracks are 70/27,218 for H2B, 70/21,535 for GRwt-Dex, and 65/35,103 for GRwt-Cort. See also Figure S4.

Figure 5.. IDR-mediated interactions affect the distribution of dwell times

(A–E) Survival distribution fit to a power law for GRwt (Dex) (A, #cells/#tracks are 70/21,535), GR-C428G (B, #cells/#tracks are 52/20,354), and GRmon (Dex) (C, #cells/#tracks are 87/19,822). Survival distribution for PPARα (D, #cells/#tracks are 60/12,237) and GR-407C (E, #cells/#tracks are 60/37,662) fit to a bi-exponential. Fits are shown in red, 95% CI of the empirical survival distributions calculated using Greenwood’s formula are indicated with dashed lines, and data points are shown as solid circles. (F) Schematic pipeline for splitting chromatin-bound and confined tracks. Tracks are classified based on the posterior probability to belong to a particular state, which is then used to calculate the weighted dwell-time distribution for each binding state. (G) GRwt (Dex) survival distribution for trajectories belonging to confinement (red) and chromatin binding (green) states fit to power laws (solid lines, #cells/#tracks are 70/21,535). (H) Survival distributions of the confined population for GRwt (Dex) (red) and GRmon (Dex) (blue) fit to power laws (solid lines, #cells/#tracks are 87/19,822). (I) Survival distributions of the confined (red) and chromatin-bound (green) population of GRwt (Dex) and confined (blue) and chromatin-bound population (bright green) of GRdim (Dex, #cells/#tracks are 80/30,794). Solid lines show power-law fits. In all cases, error bars denote SEM. See also Figure S5.

Figure 6.. Proposed model for the modulation of gene expression by confinement and the emergence of power-law dwell-time distributions

(A) Transcription factors (TF, red spots) navigate the nucleoplasm until they find their targets. They can be freely diffusing in the nucleoplasm (isolated red spots), confined in high-density IDR-dependent hubs (shaded areas), or interacting with chromatin either specifically or non-specifically. k_off, dissociation rate from chromatin; k, dissociation rate from the confined region. (B) Confined regions concentrate TFs, reducing the search time (i.e., greater k_on, thicker arrow). Hence, transcriptional activity is potentiated compared to a gene whose enhancer element is not located in a confined region. (C) Broadly distributed binding affinities of a TF (dashed line) are composed of binding distributions arising from different chromatin environments and/or motifs (solid lines, top graph). Similarly, a confined transcription factor can exhibit a broad distribution of effective binding affinities related to the time that it takes to escape the confinement region, which depends on the size and physical properties of the hub (solid lines, bottom graph). (D) For a heavy-tailed distribution of binding affinities and confinement, the dwell-time distribution is expected to follow a power law. In the case of GR, the confinement dwell times are longer than for chromatin binding (as depicted). However, other TFs might present the opposite behavior if they have larger binding affinities.