Browsing by Author "Dickson, Bradley M."

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    Global lysine methylome profiling using systematically characterized affinity reagents
    (Springer Nature, 2023-01-07) Berryhill, Christine A.; Hanquier, Jocelyne N.; Doud, Emma H.; Cordeiro‑Spinetti, Eric; Dickson, Bradley M.; Rothbart, Scott B.; Mosley, Amber L.; Cornett, Evan M.; Biochemistry and Molecular Biology, School of Medicine
    Lysine methylation modulates the function of histone and non-histone proteins, and the enzymes that add or remove lysine methylation—lysine methyltransferases (KMTs) and lysine demethylases (KDMs), respectively—are frequently mutated and dysregulated in human diseases. Identification of lysine methylation sites proteome-wide has been a critical barrier to identifying the non-histone substrates of KMTs and KDMs and for studying functions of non-histone lysine methylation. Detection of lysine methylation by mass spectrometry (MS) typically relies on the enrichment of methylated peptides by pan-methyllysine antibodies. In this study, we use peptide microarrays to show that pan-methyllysine antibodies have sequence bias, and we evaluate how the differential selectivity of these reagents impacts the detection of methylated peptides in MS-based workflows. We discovered that most commercially available pan-Kme antibodies have an in vitro sequence bias, and multiple enrichment approaches provide the most comprehensive coverage of the lysine methylome. Overall, global lysine methylation proteomics with multiple characterized pan-methyllysine antibodies resulted in the detection of 5089 lysine methylation sites on 2751 proteins from two human cell lines, nearly doubling the number of reported lysine methylation sites in the human proteome.
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    A physical basis for quantitative ChIP-sequencing
    (Elsevier, 2020-11-20) Dickson, Bradley M.; Tiedemann, Rochelle L.; Chomiak, Alison A.; Cornett, Evan M.; Vaughan, Robert M.; Rothbart, Scott B.; Biochemistry and Molecular Biology, School of Medicine
    ChIP followed by next-generation sequencing (ChIP-Seq) is a key technique for mapping the distribution of histone posttranslational modifications (PTMs) and chromatin-associated factors across genomes. There is a perceived challenge to define a quantitative scale for ChIP-Seq data, and as such, several approaches making use of exogenous additives, or "spike-ins," have recently been developed. Herein, we report on the development of a quantitative, physical model defining ChIP-Seq. The quantitative scale on which ChIP-Seq results should be compared emerges from the model. To test the model and demonstrate the quantitative scale, we examine the impacts of an EZH2 inhibitor through the lens of ChIP-Seq. We report a significant increase in immunoprecipitation of presumed off-target histone PTMs after inhibitor treatment, a trend predicted by the model but contrary to spike-in-based indications. Our work also identifies a sensitivity issue in spike-in normalization that has not been considered in the literature, placing limitations on its utility and trustworthiness. We call our new approach the sans-spike-in method for quantitative ChIP-sequencing (siQ-ChIP). A number of changes in community practice of ChIP-Seq, data reporting, and analysis are motivated by this work.