A noncatalytic activity of the H4K20 demethylase DPY-21 regulates condensin DC binding
Laura Breimann *, Ana Karina Morao *, Jun Kim, David Sebastian Jimenez, Nina Maryn, Krishna Bikkasani, Michael J Carrozza, Sarah E Albritton, Maxwell Kramer, Lena Annika Street, Kustrim Cerimi, Vic-Fabienne Schumann, Ella Bahry, Stephan Preibisch, Andrew Woehler, Sevinç Ercan
bioRxiv: https://www.biorxiv.org/content/10.1101/2021.04.11.438056v1; doi: https://doi.org/10.1101/2021.04.11.438056
* equal contribution
- 1. Abstract
- 2. Requirements FRAP Matlab analysis
- 3. FRAP analysis tutorial
- 4. Image intensity analysis
- 5. Worm size analysis
- 6. RNA-seq analysis
Condensin is a multi-subunit SMC complex that binds to and compacts chromosomes. Unlike cohesin, in vivo regulators of condensin binding dynamics remain unclear. Here we addressed this question using C. elegans condensin DC, which specifically binds to and represses transcription of both X chromosomes in hermaphrodites for dosage compensation. Mutants of several chromatin modifiers that regulate H4K20me and H4K16ac cause varying degrees of X chromosome derepression. We used fluorescence recovery after photobleaching (FRAP) to analyze how these modifiers regulate condensin DC binding dynamics in vivo. We established FRAP using the SMC4 homolog DPY-27 and showed that a well-characterized ATPase mutation abolishes its binding. The greatest effect on condensin DC dynamics was in a null mutant of the H4K20me2 demethylase DPY-21, where the mobile fraction of the complex reduced from ~30% to 10%. In contrast, a catalytic mutant of dpy-21 did not regulate condensin DC mobility. Separation of catalytic and non-catalytic activity is also supported by Hi-C data in the dpy-21 null mutant. Together, our results indicate that DPY-21 has a non-catalytic role in regulating the dynamics of condensin DC binding, which is important for transcription repression.
The analysis script was developed and tested in Matlab R2018a on Mac OS 10.15.7.
The following Matlab toolboxes are required to run “FRAP_analysis.m”:
- curve_fitting_toolbox
- image_toolbox
The following scripts have to be in the same folder as “FRAP_analysis.m”
- tiffread2.m (by Francois Nedelec)
- struct2.csv.m (by James Slegers)
- matVIS.m (by S. Junek)
- dftregistration.m (by Manuel Guizar-Sicairos, Samuel T. Thurman, and James R. Fienup)
- timesteps.m
- timestepsArray.m
FRAP protocol: The extended experimental protocol can be found here
A FRAP dataset of a C. elegans intestine nuclei can be found here
Step 1: Select the data for analysis
The first step after running the script is to select the input folder with the raw FRAP images and an output folder for the analysis files. A window will pop up, and you can navigate to the respective folders (first input, then output).
Choose data_tif directory
Choose matlab_results directory
Next, you need to select the FRAP dataset you want to analyze. The Leica SP8 creates two image stacks per FRAP experiment, one before the bleach point and one after the bleach point. First, select the dataset before the bleach and then click on the post-bleach image stack.
Load pre-stack
Load post-stack
Step 2: Manually outline the cell nucleus
To select only one nucleus for FRAP analysis, you can draw a ROI. For that, a window will open with a filtered post bleach image (mean of the first three images) and just start outlining the nucleus by clicking in the image. Once you are satisfied with the outline, double click in the middle of the selected ROI to accept it.
Step 3: Automatically detect the bleach point
The next step automatically detects the bleach point by automated thresholding (Otsu’s Method) of an image of the difference of the mean pre-bleach images and the mean of the first five post-bleach images. A window will appear with the pre-bleach (F_pre) and post-bleach (F_post) images and the difference between the two images (F_diff). The lower row depicts the selected mask (mask_nuc) from the previous step (thresholded) and the mask for the bleach point (mask_bl) based on F_diff above.
At this point, there is the option to change the threshold for the bleach point selection. Simply press No in the second window and write a value between 0-1 in the Command Window (the starting point is 0.6), and press enter. If you are happy with the bleachpoint detection, press Yes. The displayed overview image is saved as _mask.tif to the previously selected output folder.
Step 4: Inspect the results
The following steps are automatically executed and will save the results to the previously selected output folder.
The file _bleaching_correction.tiff shows the correction for the acquisition bleaching. Acquisition bleaching is detected in the mean intensity of the whole nucleus region of interest in the post-bleach images. This decrease in intensity is fitted with a monoexponential decay and used to correct the acquisition bleaching during fluorescence recovery. To correct for differences in initial intensity and extent of photobleaching, such that different datasets could be directly compared, each acquisition bleaching corrected curve is then normalized to an initial value of 1.
The fitted and normalized recovery curve is saved as _recovery.tif to the output folder. The graph displays the normalized fluorescence in the whole nucleus (red) and the bleach point recovery (black) fitted with a monoexponential function with nonlinear least-squares-based fitting. The immobile (fim) and mobile fractions (fmo) are displayed in the image. The recovery time constant (𝜏) and t-half (t_0.5) values from the fit of the curve. The fitting of the curve can only be changed directly in the MATLAB script (Section 7).
To check how well the fit describes the observation, a set of goodness of fit values is saved in the _gof.csv file in the results folder. It contains different statistics:
- The sum of squares due to error (SSE) (values closer to 0 are good)
- R-square (values closer to 1 are good)
- Degrees of Freedom (DFE)
- Adjusted R-square (values closer to 1 are good)
- Root mean squared error (RMSE) (values closer to 0 are good)
These values can be used to select the best fit or filter data.
For further analysis and averaging of different experiments, the normalized values for the FRAP curve, and the tau value and percent of the immobile and mobile fractions are saved to the file _pyan.txt. The data structure is as follows: the first value is the tau-value, then the mobile fraction and the immobile fraction. From the 4th value on, the normalized FRAP recovery values are listed.
The t-half value is calculated by two different approaches.
Firstly, using the fit of the recovery curve the fluorescence intensity at the half-maximum timepoint of the fit is calculated and saved as _t_half_value_from_fit.txt.
Secondly, the more direct way is to calculate the fluorescence intensity at the half-maximum timepoint without using the fit. A visual representation of this can be found in the image _thalf_no_fit.tif and the estimated value in _t_half_value_from_fit.txt
Step 5: Plotting results and comparing datasets
Example scripts for plotting FRAP curves for different datasets using Python can be found here
The Python script used for Image intensity analysis can be found here
The Python script used for worm size analysis can be found here
The R script used for RNA-seq analysis can be found here