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are inventors on patent applications related to this work. S.V.B. and D.D.D.C. are co-founders and provide consulting for DNAMx, Inc. S.V.B. is co-inventor on patents relating to mutation-based ctDNA detection technology licensed to Roche Molecular Diagnostics. D.D.D.C. and S.V.B. have received institutional research funding from Nektar Therapeutics.Research.

Transcription-related proteins are frequently identified as targets of sumoylation, including multiple subunits of the RNA polymerase II (RNAPII) general transcription factors (GTFs). However, it is not known how sumoylation affects GTFs or whether they are sumoylated when they assemble at promoters to facilitate RNAPII recruitment and transcription initiation. To explore how sumoylation can regulate transcription genome-wide, we performed SUMO ChIP-seq in yeast and found, in agreement with others, that most chromatin-associated sumoylated proteins are detected at genes encoding tRNAs and ribosomal proteins (RPGs). However, we also detected 147 robust SUMO peaks at promoters of non-ribosomal protein-coding genes (non-RPGs), indicating that sumoylation also regulates this gene class. Importantly, SUMO peaks at non-RPGs align specifically with binding sites of GTFs, but not other promoter-associated proteins, indicating that it is GTFs specifically that are sumoylated there. Predominantly, non-RPGs with SUMO peaks are among the most highly transcribed, have high levels of TFIIF, and show reduced RNAPII levels when cellular sumoylation is impaired, linking sumoylation with elevated transcription. However, detection of promoter-associated SUMO by ChIP might be limited to sites with high levels of substrate GTFs, and promoter-associated sumoylation at non-RPGs may actually be far more widespread than we detected. Among GTFs, we found that TFIIF is a major target of sumoylation, specifically at lysines 60/61 of its Tfg1 subunit, and elevating Tfg1 sumoylation resulted in decreased interaction of TFIIF with RNAPII. Interestingly, both reducing promoter-associated sumoylation, in a sumoylation-deficient Tfg1-K60/61R mutant strain, and elevating promoter-associated SUMO levels, by constitutively tethering SUMO to Tfg1, resulted in reduced RNAPII occupancy at non-RPGs. This implies that dynamic GTF sumoylation at non-RPG promoters, not simply the presence or absence of SUMO, is important for maintaining elevated transcription. Together, our findings reveal a novel mechanism of regulating the basal transcription machinery through sumoylation of promoter-bound GTFs.

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The small ubiquitin-like modifier (SUMO) is implicated in various cellular activities, including transcriptional regulation. We previously showed that the yeast activator Gcn4 becomes sumoylated during activation, facilitating its eventual promoter eviction and transcriptional shut off. Here we show that the corepressor Tup1 is sumoylated, at two specific lysines, under various stress conditions. Mutation of these sites has no effect on Tup1 recruitment or RNAP II promoter occupancy immediately following induction. However, Tup1 levels subsequently decrease, while RNAP II and transcription increase in Tup1 mutant cells. Consistent with this, a Tup1 mutant displaying increased sumoylation led to reduced transcription. We also show that coordinated sumoylation of Gcn4 and Tup1 enhances Gcn4 promoter eviction, and that multiple Tup1-interacting proteins become sumoylated after stress. Together, our studies provide evidence that coordinated sumoylation of Gcn4, Tup1, and likely other factors, dampens activated transcription by stabilizing Tup1 binding and stimulating Gcn4 and RNAP II removal.

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BACKGROUND: Cell-free methylation DNA immunoprecipitation-sequencing (cfMeDIP-seq) identifies genomic regions with DNA methylation, using a protocol adapted to work with low-input DNA samples and with cfDNA. This method allows for DNA methylation profiling of circulating tumour DNA in cancer patients' blood samples. Such epigenetic profiling of circulating tumour DNA provides information about in which tissues tumour DNA originates, a key requirement of any test for early cancer detection. In addition, DNA methylation signatures provide prognostic information and can detect relapse. For robust quantitative comparisons between samples, immunoprecipitation enrichment methods like cfMeDIP-seq require normalization against common reference controls. METHODS: To provide a simple and inexpensive reference for quantitative normalization, we developed a set of synthetic spike-in DNA controls for cfMeDIP-seq. These controls account for technical variation in enrichment efficiency due to biophysical properties of DNA fragments. Specifically, we designed 54 DNA fragments with combinations of methylation status (methylated and unmethylated), fragment length (80 bp, 160 bp, 320 bp), G+C content (35%, 50%, 65%), and fraction of CpG dinucleotides within the fragment (1/80 bp, 1/40 bp, 1/20 bp). We ensured that the spike-in synthetic DNA sequences do not align to the human genome. We integrated unique molecular indices (UMIs) into cfMeDIP-seq to control for differential amplification after enrichment. To assess enrichment bias according to distinct biophysical properties, we conducted cfMeDIP-seq solely on spike-in DNA fragments. To optimize the amount of spike-in DNA required, we added varying quantities of spike-in control DNA to sheared HCT116 colon cancer genomic DNA prior to cfMeDIP-seq. To assess batch effects, three separate labs conducted cfMeDIP-seq on peripheral blood plasma samples from AML patients. RESULTS: We show that cfMeDIP-seq enriches for highly methylated regions, capturing ≥99.99% of methylated spike-in control fragments with ≤0.01% non-specific binding and preference for both high G+C content fragments and fragments with more CpGs. The use of 0.01 ng of spike-in control DNA total provided sufficient sequencing reads to adjust for variance due to fragment length, G+C content, and CpG fraction. Using the known amount of each spiked-in fragment, we created a generalized linear model that absolutely quantifies molar amount from read counts across the genome, while adjusting for fragment length, G+C content, and CpG fraction. Employing our spike-in controls greatly mitigates batch effects, reducing batch-associated variance to ≤1% of the total variance within the data. DISCUSSION: Incorporation of spike-in controls enables absolute quantification of methylated cfDNA generated from MeDIP-seq experiments. It mitigates batch effects and corrects for biases in enrichment due to known biophysical properties of DNA fragments and other technical biases. We created an R package, spiky, to convert read counts to picomoles of DNA fragments, while adjusting for fragment properties that affect enrichment. The spiky package is available on GitHub (https://github.com/trichelab/spiky) and will soon be available on Bioconductor.

Supplementary Figure from Tumor-Naïve Multimodal Profiling of Circulating Tumor DNA in Head and Neck Squamous Cell Carcinoma