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Kinetic principles underlying pioneer function of GAGA transcription factor in live cells

Abstract

How pioneer factors interface with chromatin to promote accessibility for transcription control is poorly understood in vivo. Here, we directly visualize chromatin association by the prototypical GAGA pioneer factor (GAF) in live Drosophila hemocytes. Single-particle tracking reveals that most GAF is chromatin bound, with a stable-binding fraction showing nucleosome-like confinement residing on chromatin for more than 2 min, far longer than the dynamic range of most transcription factors. These kinetic properties require the full complement of GAF’s DNA-binding, multimerization and intrinsically disordered domains, and are autonomous from recruited chromatin remodelers NURF and PBAP, whose activities primarily benefit GAF’s neighbors such as Heat Shock Factor. Evaluation of GAF kinetics together with its endogenous abundance indicates that, despite on–off dynamics, GAF constitutively and fully occupies major chromatin targets, thereby providing a temporal mechanism that sustains open chromatin for transcriptional responses to homeostatic, environmental and developmental signals.

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Fig. 1: Chromatin-binding dynamics of GAF shown by SPT in live Drosophila hemocytes.
Fig. 2: POZ, Q-rich and DBD domains of GAF all contribute to stable chromatin binding and long residence times.
Fig. 3: Chromatin binding by GAF is largely independent of remodelers PBAP and NURF.
Fig. 4: Heat shock increases chromatin-binding fraction of HSF without affecting dwell time.
Fig. 5: High site occupancy and remodeler autonomy quantifies pioneering criteria.
Fig. 6: Pioneering of chromatin accessibility is a process involving multiple inputs.

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Data availability

All raw SPT trajectory coordinates have been deposited at the 4D Nucleome Data Portal: https://data.4dnucleome.org/tang_et_al_2021. Due to the large number and size of files, original videos will be available upon request. GSE157235 and GSE149336 are the published ChIP–seq and ATAC-seq data sets, respectively, used for analysis in this study and both are available at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE149339.Source data are provided with this paper.

Code availability

Custom R package Sojourner used to process and analyze SPT trajectories is available at https://github.com/sheng-liu/sojourner. Python codes for running Spot-On were adapted from https://gitlab.com/tjian-darzacq-lab/Spot-On-cli to analyze SPT trajectories. MATLAB codes for running vbSPT were adapted from https://gitlab.com/anders.sejr.hansen/anisotropy to classify trajectories.

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Acknowledgements

We thank A. Hansen and M. Woringer for discussion on Spot-On analyses, A. Hansen, C. Cattoglio, X. Darzacq and R. Tjian for the CTCF-Halo U2OS cell line and discussions on measuring factor abundance, T. Lionnet, J.Z. Liu, P. Dong and B. Mehl for advice on SPT, Y.H. Ling and S.J. Yoo for assistance with data analysis, P. Vallotton for customization of DiaTrack software, P. Badenhorst for advice on larval hemocyte culture, S. Deluca for advice on bioinformatics, W. Dai for imaging assistance, E. Pryce and the Integrated Imaging Center for LSM 800 training, Y. Liu for assembly and maintenance of a high performance computational platform, J. Lis, M. Levine and G. Hager for discussion, and Wu Laboratory members for comments on the manuscript. This study was supported by HHMI funding to the TIC (C.W., Q.Z. and L.L.), Johns Hopkins Bloomberg Distinguished Professorship funds (C.W.), and National Institutes of Health grant nos. GM132290-01 (C.W.) and DK127432 (C.W.).

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Authors and Affiliations

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Contributions

X.T. performed all genetic and imaging experiments with support from T.L., J.W. and Y.R. and all data analysis using R functions created by S.L. and X.T. L.D.L. and Q.Z. synthesized JF552/JFX554 and JF700. X.T. and C.W. designed the study and wrote the paper with input from all authors.

Corresponding author

Correspondence to Carl Wu.

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L.D.L. and Q.Z. are listed as inventors on patents and patent applications whose values might be affected by publication. The remaining authors declare no competing interests.

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Nature Structural & Molecular Biology thanks Mounia Lagha and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Carolina Perdigoto, in collaboration with the Nature Structural & Molecular Biology team.

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Extended data

Extended Data Fig. 1 Hemocyte imaging and fast-tracking diffusive parameters for Halo-GAF, GAFL-Halo, GAFS-Halo and Halo-H2B.

(A) Experimental timeline of single-particle imaging with 3rd instar larval hemocytes. 3rd instar larvae are washed with DI H2O (left) and dissected in a coverglass bottom dish containing Schneider’s medium and JF dye at room temperature. Upon dissection hemocytes are released into the medium and labeled for 30 min, the rest of the larval tissues are discarded (middle). Cells are briefly washed twice with fresh media and imaged for 1–2 h. (B) Spot-On fits of Halo-GAF, GAFL-Halo, GAFS-Halo, Halo-H2B fast-tracking data. (C) Spot-On kinetic modeling of fast-tracking data shows 77% of Halo-GAF is chromatin bound. Similar values are obtained for isoforms GAFL and GAFS individually tagged in the presence of untagged GAF isoforms. Results are mean ± SD from three biological replicates. (D) Chromatin-free fraction (Ffree), long- and short-lived chromatin-binding fractions (Fsb and Ftb) of HaloTagged GAF fusions extracted from fast-tracking in (C, n = 3 biological replicates for Halo-GAF and Halo-H2B, n = 4 biological replicates for the rest conditions) and slow-tracking data (Extended Data Fig. 2e, n = 100 resamplings), respectively, with error propagation. Results are mean ± SD. (E) Diffusion coefficients of bound fraction (Dbound) for Halo-GAF, GAFL-Halo, GAFS-Halo, Halo-H2B derived by Spot-On. Results are mean ± SD (n = 3 biological replicates for Halo-GAF and Halo-H2B, n = 4 biological replicates for the rest conditions). (F) Diffusion coefficients of free fraction (Dfree) for Halo-GAF, GAFL-Halo, GAFS-Halo, Halo-H2B derived by Spot-On. Results are mean ± SD (n = 3 biological replicates for Halo-GAF and Halo-H2B, n = 4 biological replicates for the rest conditions).

Extended Data Fig. 2 Slow tracking results for Halo-GAF, GAFL-Halo, GAFS-Halo and Halo-H2B.

(A) Fast and slow tracking regimes. Fast tracking with 10-ms frame rate and high laser power allows single molecule imaging to distinguish slow (chromatin-bound) and fast (chromatin-free) diffusing subpopulations. Slow tracking uses low-intensity excitation and 500 ms exposure time to motion blur diffusing molecules and selectively observe the dwell times of chromatin-bound molecules. A higher concentration of JF700 is added to block labeling of most HaloTag protein fusions, while a much lower concentration of JF552 is used to sparsely label a small fraction of HaloTag so that each nucleus shows only 2~10 molecules per frame during image acquisition. (B) Kymograph of a Halo-GAF slow tracking movie shows traces of bound GAF molecules over time (upper). Trajectories identified from the raw movie are plotted on the kymograph using separate colors (lower). (C) Kymograph of a Halo-H2B slow tracking movie shows traces of bound H2B molecules over time (upper). Trajectories identified from the raw movie are plotted on the kymograph (lower). (D) Survival probability curves (1-CDF) plotted from apparent dwell times of thousands (n) of single-molecule chromatin-binding events for Halo-GAF, GAFL-Halo and GAFS-Halo. (E) One-component and two-component exponential fit of survival probabilities (1-CDF) from slow tracking data (with 95% CI, confidence interval) of Halo-GAF, GAFL-Halo and GAFS-Halo. Pie charts show the stable-binding (fsb) and transient-binding (fsb) fractions derived from two-component fits, and errors represent bootstrapped SD (n = 100 resamplings). (F) Corrected average residence times for stable- (τsb) and transient- (τtb) binding by transgenic GAFL-Halo and GAFS-Halo. Error bars represent bootstrapped SD after resampling 100 times (n = 100).

Extended Data Fig. 3 Generation of mutations in functional domains of Halo-GAF by CRISPR/Cas9 gene editing.

(A) In the Halo-GAF fly strain, Cas9 and gRNA were introduced to target the BTB/POZ domain, zinc finger and Q-rich domains, respectively. The BTB/POZ domain is separated by a large intron. A gRNA target site in the second exon (orange scissors) was selected and a donor plasmid containing a 90 bp deletion (ΔPOZ) was constructed for homology-directed repair (HDR). For zinc finger mutations, we selected a gRNA target site in the zinc finger coding region (green scissors) and screened for in-frame small deletions generated by non-homologous end joining (NHEJ). To generate deletions of both Q-rich domains in long and short isoforms (ΔQ), two gRNAs targeting the upstream ends of two Q-rich domains (pink scissors) were introduced at the same time, and we screened for double frame-shift deletions induced by NHEJ. Half arrows indicate positions of the PCR primers used in (B). TSS, transcription start site. (B) PCR validation of ΔPOZ. Lanes 1–3 show two PCR bands indicating precise deletion in one allele; lanes 4–5 are two lines without the precise deletion. Sanger sequencing verified a precise 90 bp deletion in one allele. (C) AlphaFold121 predicted structure for a homodimer of GAF POZ domains (residues 1–120) is highly similar to published crystal structures of PLZF POZ domains122,123. A 90-bp deletion in the second exon generates a 30-AA deletion (Δ90-119, ΔPOZ, orange), which includes a β sheet that mediates two of three principal contacts stabilizing the dimer (dashed circles). This functionally lethal mutation is likely to impair GAF multimerization, although the degree to which the multimerization is reduced is unclear. N and C indicate two termini of one monomer; N’ and C’ for the other monomer. (D) Amino acid sequence of GAF DNA binding domain, which contains a single C2H2 zinc finger (green rectangle) and two upstream basic regions (BR1 and BR2, yellow rectangle). Amino acids involved in recognizing the GAGAG consensus sequence are underlined56. Two zinc finger mutations were isolated and verified by sanger sequencing, ZF9 and ZF10, with R356 and N357, or R356 deleted, respectively. (E) Amino acid sequence of GAF Q-rich domains for long and short isoforms. In ΔQ, a 7 bp deletion was identified by sanger sequencing in both isoforms at the upstream ends of Q-rich domains, resulting in frameshifts and truncations of the Q-rich domains from both isoforms. P403 in the long isoform and A440 in the short isoform are deleted and the subsequent amino acids are newly introduced by the frame shifts.

Source data

Extended Data Fig. 4 Live-cell SPT and FRAP diffusive parameters for Halo-GAF mutants.

(A) Spot-On fits of fast-tracking data for Halo-GAF mutants (see Extended Data Fig. 1b for WT). (B) Survival probability curves (1-CDF) from apparent dwell times of >1,000 single-molecule chromatin-binding events, for WT and mutant Halo-GAF. (C) One-component and two-component exponential fit of survival probabilities (1-CDF) from slow tracking data (with 95% CI, confidence interval) of Halo-GAF mutants (see Extended Data Fig. 2e for WT). Pie charts show the stable-binding (fsb) and transient-binding (fsb) fractions derived from two-component fits. (D) Diffusion coefficients of bound fraction (Dbound) for Halo-GAF and Halo-H2B derived by Spot-On. Results are mean ± SD (n = 3 biological replicates for Halo-GAF WT, ΔQ and Halo-H2B, n = 4 biological replicates for the remaining conditions). (E) Diffusion coefficients of free fraction (Dfree) for Halo-GAF and Halo-H2B derived by Spot-On. Results are mean ± SD (n = 3 biological replicates for Halo-GAF WT, ΔQ and Halo-H2B, n = 4 biological replicates for the remaining conditions). (F) Average MSD versus lag time for WT and Halo-GAF mutants at 500-ms frame rate. Mean and SE (shaded) are shown. System noise is shown by the MSD of dye molecules stuck on coverglass. (G) Mean fluorescence recovery curves from FRAP experiments for Halo-GAF WT and mutants in hemocytes labeled with 50 nM JF552. Shaded areas represent SE. (H) Half recovery times (thalf) of FRAP experiments.

Extended Data Fig. 5 Live-cell SPT diffusive parameters for GAFL-Halo and GAFS-Halo in bap170 and nurf301 mutants.

(A) Spot-On fits of fast-tracking data for GAFL-Halo and GAFS-Halo in bap170 and nurf301 mutants (see Extended Data Fig. 1b for WT). See methods for genotypes of WT, bap170 and nurf301. (B) Survival probability curves (1-CDF) from apparent dwell times of more >1,000 single-molecule chromatin-binding events for GAFL-Halo in WT, bap170 and nurf301 mutants. (C) Survival probability curves (1-CDF) from apparent dwell times of more >1,000 single-molecule chromatin-binding events for GAFS-Halo in WT, bap170 and nurf301 mutants. (D) One-component and two-component exponential fit of survival probabilities (1-CDF) from slow tracking data (with 95% CI, confidence interval) for GAFL-Halo and GAFS-Halo in bap170 and nurf301 mutants (see Extended Data Fig. 2d for WT). Pie charts show the stable-binding (fsb) and transient-binding (fsb) fractions derived from two-component fits. (E) Diffusion coefficients of bound fraction (Dbound) for GAFL-Halo, GAFS-Halo and Halo-H2B derived by Spot-On. Results are mean ± SD (n = 3 biological replicates for GAFL-Halo in nurf301 mutant and Halo-H2B, and n = 4 biological replicates for the remaining conditions). (F) Diffusion coefficients of free fraction (Dfree) for GAFL-Halo, GAFS-Halo and Halo-H2B derived by Spot-On. Results are mean ± SD (n = 3 biological replicates for GAFL-Halo in nurf301 mutant and Halo-H2B, and n = 4 biological replicates for the remaining conditions).

Extended Data Fig. 6 Comparison of GAF ChIP-seq signals in LacZ RNAi (control) and Bap170 RNAi experiments of Judd et al. 2021.

(A) Comparison of GAF ChIP-seq signals in LacZ RNAi (control), Bap170 RNAi and GAF RNAi experiments (bw files from GSE157235) derived from Judd et al. 202128. The left graph shows mean ChIP enrichment (mean ± SE) for all regions ±1 kb centered around transcription start sites (TSS); The right graphs show heat maps of all genes (generated by computeMatrix/plotHeatmap of deepTools124). Dashed rectangle indicates the top 10% regions with the highest GAF ChIP enrichment in control (for which the mean enrichment is plotted in (B)). (B) Comparison of GAF ChIP-seq, and ATAC-seq in LacZ RNAi (control), Bap170 RNAi and GAF RNAi experiments (bw files from GSE157235, and GSE149336, respectively) derived from Judd et al.28. Regions ±1 kb flanking TSS were sorted according to mean GAF ChIP enrichment in LacZ RNAi from high to low as shown in (A). Mean values of GAF ChIP enrichment (left column) and ATAC-seq (right column) enrichment are plotted for the top 1%, 5%, 10% of regions with the highest GAF ChIP signal and for the remaining 90% regions. For 3616 TSS-flanking regions with highest GAF ChIP enrichment, on average, chromatin accessibilities (ATAC-seq) are reduced in both Bap170 RNAi and GAF RNAi conditions, while the mean enrichment for GAF ChIP-seq shows no change in Bap170 RNAi. These analyses indicate that although there are differential effects at specific sites, the overall genome-wide GAF chromatin binding is not affected in PBAP-depleted condition.

Extended Data Fig. 7 HSF-Halo binding on polytene chromosomes and live-cell SPT at RT and HS conditions.

(A) Confocal images of HSF-Halo in fixed salivary glands. HSF-Halo is mostly nucleoplasmic at room temperature (RT) and binds to many loci after heat shock (HS) at 37.5 °C for 10 and 30 min. Maximum projections of confocal z-stacks are shown because major HSF bands are located in different focal planes. The pattern of HSF binding on heat shock is substantially reduced in trl and nurf301 mutants and partially affected in the bap170 mutant. Polytene loci showing little or no change of HSF binding in the trl mutant is consistent with findings that not all HS genes are GAF-dependent18. (These genes presumably require an analogous pioneer factor and attendant remodelers). See methods for genotypes of WT, trl, bap170 and nurf301. (B) Spot-On fits of fast-tracking data for HSF-Halo (RT, 37.5 °C) and Halo-GAF (37.5 °C, see Extended Data Fig. 1b for RT). (C) Survival-probability curves (1-CDF) from apparent dwell times of >1,000 single-molecule chromatin-binding events for HSF-Halo at RT and 37.5 °C. (D) Survival-probability curves (1-CDF) from apparent dwell times of >1,000 single-molecule chromatin-binding events for Halo-GAF at RT and 37.5 °C. (E) One-component and two-component exponential fit of survival probabilities (1-CDF) from slow tracking data (with 95% CI, confidence interval) for HSF-Halo (RT, 37.5 °C) and Halo-GAF (37.5 °C, see Extended Data Fig. 2d for RT). Pie charts show the stable-binding (fsb) and transient-binding (fsb) fractions derived from two-component fits.

Extended Data Fig. 8 vbSPT analysis of fast-tracking data for HSF-Halo and Halo-GAF at RT and HS conditions.

(A) Overview of fast-tracking trajectory classification with displacement-based HMM classification (vbSPT). After assigning each displacement as either in bound or free state, each trajectory is sub-classified as ‘bound’ or ‘free’, a small fraction of trajectories containing 2 states are excluded from the following analysis in (B–G) and (Fig. 5). (B) Violin plots of displacements show distinct distributions for bound and free trajectories classified by vbSPT. (C) Examples of bound trajectories at 10-ms frame rate classified by vbSPT for HSF-Halo, Halo-GAF at RT and 37.5 °C and Halo-H2B at RT. (D) Examples of free trajectories at 10-ms frame rate classified by vbSPT for HSF-Halo, Halo-GAF at RT and 37.5 °C and Halo-H2B at RT.

Extended Data Fig. 9 MSD analysis of vbSPT-classified HSF-Halo and Halo-GAF fast-tracking trajectories.

(A) Plot of average MSD as a function of lag time Δt of bound trajectories classified by vbSPT for HSF-Halo, Halo-GAF at RT and 37.5 °C and Halo-H2B at RT. The right panel shows a zoomed-in section of the same plot. System noise is shown by MSD of dye molecules stuck on the coverglass. Mean and SE (shaded) are shown. (B) Average MSD over Δt of bound trajectories at 10-ms frame rate classified by vbSPT for Halo-GAF WT and mutants, and Halo-H2B. Mean and SE (shaded) are shown. (C) Average MSD over Δt of free trajectories at 10-ms frame rate classified by vbSPT for Halo-GAF WT and mutants, and Halo-H2B. Mean and SE (shaded) are shown. (D) Average MSD over Δt of free trajectories at 10-ms frame rate classified by vbSPT for HSF-Halo, Halo-GAF at RT and 37.5 °C and Halo-H2B at RT. Mean and SE (shaded) are shown. (E) Radius of confinement (Rc) is derived by fitting individual MSD curves with a confined diffusion model, for bound trajectories at 10-ms frame rate classified by vbSPT, for HSF-Halo, Halo-GAF at RT and 37.5 °C, and Halo-H2B at RT. (F) Average MSD over Δt for WT and Halo-GAF mutants at 500-ms frame rate. Mean and SE (shaded) are shown.

Extended Data Fig. 10 FACS quantitation of Halo-GAF and HSF-Halo in Drosophila hemocytes and cell cycle phase identification.

(A) Total Halo-GAF (knock-in WT, ΔPOZ and ΔQ) and HSF-Halo (transgenic in the P{PZ}Hsf03091/Hsf3 background) fluorescence per cell for JF552-stained late 3rd instar larval hemocytes and CTCT-Halo in U2OS cells quantified by flow cytometry. Cellular abundance of Halo-GAF and HSF-Halo molecules are estimated using CTCF-Halo in U2OS cells as a standard (see methods)87,119. Hemocytes (w1118 strain) or U2OS cells not expressing HaloTag were used as controls for background subtraction. One of three representative flow cytometry experiments is shown. Mean ± SD of estimated protein abundance is shown at the upper left corner of each plot. A much larger number of molecules (in the order of one million) for GAF was reported earlier in the S2 cell line125; the reason for the discrepancy is unclear. FSC-A, forward scatter area. (B) Conceptual diagram of the Fly-FUCCI system126. Both GFP-E2F11–230 and mRFP1-CycB1–266 are expressed with the GAL4/UAS system. In early M phase, both GFP-E2F11–230 and mRFP1-CycB1–266 are present and thus display yellow. In mid-mitosis, the APC/C marks mRFP1-CycB1–266 for proteasomal degradation, leaving the cells fluorescing green. As cells progress from G1 to S phase, CRL4Cdt2 degrades GFP-E2F11–230, and cells are labeled red due to the presence of mRFP1-CycB1–266 only. After cells enter G2 phase, GFP-E2F11–230 protein levels reaccumulate, marking the cells yellow due to the presence of mRFP1-CycB1–266. (C) Characterization of cell-cycle stage for late 3rd instar larval hemocytes. Only 4 out of 96 cells in the field of view show ‘red only’ fluorescence (S phase), and 2 cells have ‘green only’ fluorescence (M to G1 phase). The majority of hemocytes have both red and green fluorescence, indicating G2 phase or early M phase. Given that a previous study shows only 0.32% of larval hemocytes stain positive with the mitotic phosH3 antibody127, we conclude that most larval hemocytes are in the G2 phase.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2, Videos 1 and 2 and Table 1.

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Supplementary Video 1

Fast-tracking video of Halo-GAF. A fast-tracking movie of Halo-GAF labeled with 1 nM JF554. The video was acquired with 10 ms camera integration time for single-molecule tracking after 10–30 s of initial nuclear glow.

Supplementary Video 2

Slow-tracking video of GAFL-Halo. A low-tracking video of GAFL-Halo labeled with 0.05 nM JF552 (and 50 nM nonfluorescent JF700 blocker). Images acquired at 500 ms exposure time to motion blur diffusing molecules and selectively observe chromatin-bound molecules. Video frames are placed on a 3D axis of time and x,y coordinates to display identified trajectories. Tracking parameters are adjusted to avoid identification of blurred molecules.

Supplementary Data 1

Unprocessed DNA agarose gel for Supplementary Fig. 1d.

Source data

Source Data Extended Data Fig. 3

Unprocessed DNA agarose gel for Extended Data Fig. 3b.

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Tang, X., Li, T., Liu, S. et al. Kinetic principles underlying pioneer function of GAGA transcription factor in live cells. Nat Struct Mol Biol 29, 665–676 (2022). https://doi.org/10.1038/s41594-022-00800-z

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