Inhibition of MYC by the SMARCB1 tumor suppressor

SMARCB1 encodes the SNF5 subunit of the SWI/SNF chromatin remodeler. SNF5 also interacts with the oncoprotein transcription factor MYC and is proposed to stimulate MYC activity. The concept that SNF5 is a coactivator for MYC, however, is at odds with its role as a tumor-suppressor, and with observations that loss of SNF5 leads to activation of MYC target genes. Here, we reexamine the relationship between MYC and SNF5 using biochemical and genome-wide approaches. We show that SNF5 inhibits the DNA-binding ability of MYC and impedes target gene recognition by MYC in cells. We further show that MYC regulation by SNF5 is separable from its role in chromatin remodeling, and that reintroduction of SNF5 into SMARCB1-null cells mimics the primary transcriptional effects of MYC inhibition. These observations reveal that SNF5 antagonizes MYC and provide a mechanism to explain how loss of SNF5 can drive malignancy.

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

Data analysis
After adapter trimming and low quality sequence removal by cutadapt, PRO-Seq reads longer than 15bp were reversed complemented using FastX tools. Reverse complements of the trimmed reads were aligned to the human genome hg19 using Bowtie2. Reads mapped to rRNA loci and reads with mapping quality less than 10 were removed. Bam files were given to NRSA (http://bioinfo.vanderbilt.edu/ NRSA/) to estimate RNA polymerase abundance in proximal-promoter and gene body regions of genes, to calculate pausing index and pausing index alterations, and to detect enhancers and quantify eRNA changes. ChIP-Seq reads were aligned to the hg19 genome assembly using Bowtie2. Peaks were called by MACS2 with a q-value of 0.01. Differential binding peaks were identified using DiffBind based on consensus peaks occurring at least two samples. Adapter sequences of ATAC-Seq reads were trimmed by Cutadapt (cutadapt -a CTGTCTCTTA TACACATCT-minimum-length 15 -pairedoutput), then aligned to the human genome hg38 using Bowtie2 (bowtie2 -p 8 -X 2000 -q --no-mixed --no-discordant). Peaks were called using MACS2 with q-value of 0.001 (callpeak -q 0.001 -nomodel -extsize 140). Peaks were annotated and enriched motifs were identified by HOMER. Differential enriched peaks were identified using DiffBind based on consensus peaks occurring at least two samples.
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Life sciences study design
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Sample size
No statistical methods were used to determine the sample sizes. Our sample sizes are similar to those generally employed in the field and as extensively used in our previously published studies. Typically three biological replicates were used in our studies. The exact n values for each experiment can be found in the figure legends.
Data exclusions No data were excluded from this analysis.

Replication
All replication attempts were successful. The number of times an experiment was repeated (biological replicates) and exact n values are indicated in the figure legends and/or methods section.
Randomization Randomization of samples in groups is not relevant to this study as no animal or clinical work was performed.

Blinding
For soft agar assays, the investigator counting colonies was blinded to the identification of each well.

Reporting for specific materials, systems and methods
We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material, system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response. Validation Details for validation are available on the manufacturers' websites.

October 2018
and differential peaks were determined by DESeq2 (Love et al., 2014), which calculated the log2 fold changes, Wald test pvalues, and adjusted p-values (False Discovery Rate, FDR) by the Benjamini-Hochberg procedure. Significantly changed peaks were assessed with FDR<0.05.

Data quality
FastQC was used to check sequencing quality, mapping quality was ensured by uniquely mapping and duplication level, and peak quality was measured by peak enrichment at promoter and 5' UTR regions. MYC EGFP rep1: 940 peaks were identified at FDR 5%. Of these 679 were show over five fold enrichment over input. MYC EGFP rep2: 5396 peaks were identified at FDR 5%. Of these 2945 were show over five fold enrichment over input. MYC OMOMYC rep1: 1 peaks were identified at FDR 5%. Of these 1 were show over five fold enrichment over input. MYC OMOMYC rep2: 1 peaks were identified at FDR 5%. Of these 1 were show over five fold enrichment over input. MYC SNF5 rep1: 137 peaks were identified at FDR 5%. Of these 102 were show over five fold enrichment over input. MYC SNF5 rep2: 27 peaks were identified at FDR 5%. Of these 17 were show over five fold enrichment over input. The axis labels state the marker and fluorochrome used (e.g. CD4-FITC).

Software
The axis scales are clearly visible. Include numbers along axes only for bottom left plot of group (a 'group' is an analysis of identical markers).
All plots are contour plots with outliers or pseudocolor plots.
A numerical value for number of cells or percentage (with statistics) is provided.

Methodology
Sample preparation Sample preparation is described in the Methods.

Instrument
Becton Dickinson LSRFortessa instrument Software BD FACSDDiva 8.0 software was used to analyze all flow cytometry data.
Cell population abundance Cells were analyzed without sorting.

Gating strategy
For all flow cytometry experiments, on the SSC/FSC plots the dense population on both axes was used to select for single cells.
For determining GFP-positive cells, an untransduced cell line control was measured for baseline fluorescence. All GFP-positive cells were gated against the untransduced control (see example in Supplementary data).
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