DOI: 10.5061/dryad.kd51c5bgq
title: Histone acetylation readers Bdf1 and Yaf9 direct SWR1 remodeler to +1 nucleosome

description: The histone variant H2A.Z marking permissive chromatin is deposited by the
 multicomponent SWR1 chromatin remodeler, which is targeted to
 nucleosome-free promoters by a DNA length-sensing module. How SWR1 is
 directed to the flanking acetylated +1 nucleosome, its physiological
 substrate, has been enigmatic. We show by live-cell, single-molecule
 tracking that SWR1 subunits Bdf1 and Yaf9 harboring histone acetylation
 reader domains differentially regulate chromatin binding: Bdf1 promotes
 SWR1 association, while Yaf9-YEATS slows its dissociation. Notably,
 single-molecule tracking and genome-wide ChIP-exo reveal Bdf1 and Yaf9
 contributions to global SWR1 targeting and histone exchange at +1
 nucleosomes. Our findings highlight the in-cell biochemistry of histone
 readers and suggest a generalizable, two-stage mechanism wherein
 acetylated nucleosome interactions initially constrain 3D diffusion of
 SWR1 to increase local concentration, followed by stochastic 1D diffusion
 at NFRs with directional capture by acetylated +1 nucleosomes.
description: # Histone acetylation readers Bdf1 and Yaf9 direct SWR1 remodeler to +1
 nucleosome
 [https://doi.org/10.5061/dryad.kd51c5bgq](https://doi.org/10.5061/dryad.kd51c5bgq) ### Overview This dataset supports the findings of the study titled *“Histone acetylation readers Bdf1 and Yaf9 direct SWR1 remodeler to +1 nucleosome,”* which investigates how chromatin remodelers are directed to specific genomic loci via interactions with histone post-translational modifications (PTMs). Specifically, the study focuses on the SWR1 complex in budding yeast (*S. cerevisiae*), which catalyzes the ATP-dependent exchange of histone H2A with its variant H2A.Z—a key mark of transcriptionally active chromatin. Live-cell single-molecule tracking (SMT) was employed to directly observe the binding dynamics of SWR1 and H2A.Z in wild-type and various mutant yeast strains. Using fluorescently tagged Halo fusions of Swr1 and H2A.Z, and high-temporal-resolution imaging with Janelia Fluor 552 dye, the study dissected the roles of two histone reader proteins—Bdf1 (a bromodomain-containing BET family member) and Yaf9 (a YEATS domain-containing protein)—in modulating SWR1 diffusion, chromatin association, and residence time. The dataset includes nuclear-localized track data for both “fast-tracking” (10 ms exposure) and “slow-tracking” (250 ms exposure) imaging regimes. Fast-tracking captures high-resolution diffusion behavior and enables kinetic modeling of bound versus free molecular populations. Slow-tracking emphasizes stably bound chromatin interactions and allows estimation of residence times. These data provide direct evidence that Bdf1 enhances the on-rate of SWR1 chromatin association, while Yaf9 slows dissociation, thereby guiding SWR1 targeting to +1 nucleosomes and facilitating localized H2A.Z deposition. The dataset will be useful for researchers studying chromatin dynamics, histone exchange, nuclear diffusion, and the in vivo roles of histone readers. **Description of the Data** Each CSV file in this dataset contains single-molecule tracking data derived from live-cell imaging of *S. cerevisiae* strains expressing Swr1-Halo or H2A.Z-Halo fusion proteins. Cells were labeled with the Janelia Fluor 552 (JF552) HaloTag ligand and imaged under either fast- or slow-tracking regimes. Tracks were extracted from fluorescence microscopy movies using the DiaTrack software, and only trajectories within manually defined nuclear masks were retained. **Structure of each CSV file**: Each row corresponds to one localization event (i.e., the detected position of a fluorescent molecule at a given timepoint), and the columns are structured as follows: ·       **Column A**: Serial number (index for each row) ·       **Column B**: Trajectory number (identifier for each single-molecule track) ·       **Column C**: Frame number (indicates the time point in the movie when the localization occurred) ·       **Column D**: X-coordinate of the localization (in pixels, corresponding to spatial position in the image frame) ·       **Column E**: Y-coordinate of the localization (in pixels) This structure allows reconstruction of single-molecule trajectories over time for subsequent diffusion modeling or residence time analysis. To convert pixel positions into physical distances, note that each pixel corresponds to 107 nanometers (nm). **Tracking Types**: ·       **Fast-tracking (10 ms exposure)**: Suitable for determining diffusion coefficients and estimating the fraction of molecules in bound vs. free states. Can be analyzed using kinetic modeling tools like Spot-On or saSPT. ·       **Slow-tracking (250 ms exposure)**: Emphasizes long-residence-time events by motion-blurring fast-diffusing molecules. Used to extract residence time of stably bound fractions. All files were generated using consistent experimental conditions and processing parameters, and they are named to indicate the protein tracked, strain background, tracking mode, and biological replicate number (see "File Structure" section). #### File Structure Files are named using the following convention: `___Replicate.csv` ·       **Protein**: Swr1_Halo or H2AZ_Halo ·       **Strain or Mutant**: WT, swc2del, arp6del, Swr1∆11_Bdf1, Yaf9_YEATS3A, etc. ·       **Tracking Type**: Fast_Tracking or Slow_Tracking ·       **Replicate**: Biological replicate number Example: ``` Swr1_Halo_Swr1∆11_Bdf1_Yaf9_YEATS3A_Fast_Tracking_Replicate1.csv ``` ``` H2AZ_Halo_yaf9del_Fast_Tracking_Replicate2.csv ``` ``` Swr1_Halo_arp6del_Slow_Tracking_Combined.csv ``` #### Software and Analysis Recommendations ·       The CSV files are compatible with: o   **Sojourner (R)**: [https://github.com/sheng-liu/sojourner](https://github.com/sheng-liu/sojourner) for processing of single molecule datasets o   **Spot-On (Python)**: [https://spoton.berkeley.edu](https://spoton.berkeley.edu) for kinetic modeling of diffusive components o   **saSPT (MATLAB)**: [https://github.com/alecheckert/saspt](https://github.com/alecheckert/saspt) for Bayesian inference of diffusive states ·       Slow-tracking data can be analyzed to derive residence-time distributions by fitting survival probability (1-CDF) curves with a double-exponential decay model. #### File List The full list of &gt;50 CSV files is provided within the Dryad entry, including size, strain, and replicate information. Data includes both SWR1 and H2A.Z tracking in WT and the following mutants: ·       swc2∆, swc3∆, swc5∆, arp6∆, yaf9∆, swr1∆, swr1∆chz1∆ ·       Swr1∆11_Bdf1, Bdf1_BD1_2_YF ·       Yaf9_YEATS3A, yaf9∆ ·       Combined double mutants (e.g., Swr1∆11_Bdf1_Yaf9_YEATS3A) #### Additional Notes ·       This dataset is fully described in the **Materials and Methods** and **Results** sections of the associated manuscript. ·       No external sources were used to derive the data. ·       Data are not available in any other public repository.