A Mediator-cohesin axis controls heterochromatin domain formation

The genome consists of regions of transcriptionally active euchromatin and more silent heterochromatin. We reveal that the formation of heterochromatin domains requires cohesin turnover on DNA. Stabilization of cohesin on DNA through depletion of its release factor WAPL leads to a near-complete loss of heterochromatin domains. We observe the opposite phenotype in cells deficient for subunits of the Mediator-CDK module, with an almost binary partition of the genome into dense H3K9me3 domains, and regions devoid of H3K9me3 spanning the rest of the genome. We suggest that the Mediator-CDK module might contribute to gene expression by limiting the formation of dense heterochromatin domains. WAPL deficiency prevents the formation of heterochromatin domains, and allows for gene expression even in the absence of the Mediator-CDK subunit MED12. We propose that cohesin and Mediator affect heterochromatin in different ways to enable the correct distribution of epigenetic marks, and thus to ensure proper gene expression.


Statistics
For all statistical analyses, confirm that the following items are present in the figure legend, table legend, main text, or Methods section.

n/a Confirmed
The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly The statistical test(s) used AND whether they are one-or two-sided Only common tests should be described solely by name; describe more complex techniques in the Methods section.
A description of all covariates tested A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons A full description of the statistical parameters including central tendency (e.g. means) or other basic estimates (e.g. regression coefficient) AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g. confidence intervals) For null hypothesis testing, the test statistic (e.g. F, t, r) with confidence intervals, effect sizes, degrees of freedom and P value noted Give P values as exact values whenever suitable.

For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings
For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes Estimates of effect sizes (e.g. Cohen's d, Pearson's r), indicating how they were calculated Our web collection on statistics for biologists contains articles on many of the points above.

Software and code
Policy information about availability of computer code Data collection

Data analysis
For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors/reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Research guidelines for submitting code & software for further information.

Data
Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability Images for quantification purposes were acquired using the Leica Confocal microscope 63x/1.32 oil lens using LAS-AF Software (Leica). Analysis of the intensity of H3K9me3 or HP1 in the DAPI region was performed using an in-house written macro (ImageJ v2.1.0/1.53c).
Hi-C sequencing data was processed with Hi-C-Pro 2.9. We performed loop calling with HICCUPs 0.9. Hi-C data analysis was performed with GENOVA (github.com/deWitLab/GENOVA Field-specific reporting Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection.

Life sciences Behavioural & social sciences Ecological, evolutionary & environmental sciences
For a reference copy of the document with all sections, see nature.com/documents/nr-reporting-summary-flat.pdf

Life sciences study design
All studies must disclose on these points even when the disclosure is negative. For the Hi-C data two replicates were generated. Replicates were highly similar and combined into one dataset. For RNAseq experiments triplicate libraries were generated. No sample size calculations were performed. Sample sizes were chosen based on common standards of the field.
No data was excluded.
RNAseq experiments were performed at in triplicate for any given cell line. All attempts were succesful.
No randomization was performed.
Blinding is not relevant to the current study because only machine measurements were used.