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Metabolic labeling in middle-down proteomics allows for investigation of the dynamics of the histone code

Overview of attention for article published in Epigenetics & Chromatin, July 2017
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Title
Metabolic labeling in middle-down proteomics allows for investigation of the dynamics of the histone code
Published in
Epigenetics & Chromatin, July 2017
DOI 10.1186/s13072-017-0139-z
Pubmed ID
Authors

Simone Sidoli, Congcong Lu, Mariel Coradin, Xiaoshi Wang, Kelly R. Karch, Chrystian Ruminowicz, Benjamin A. Garcia

Abstract

Middle-down mass spectrometry (MS), i.e., analysis of long (~50-60 aa) polypeptides, has become the method with the highest throughput and accuracy for the characterization of combinatorial histone posttranslational modifications (PTMs). The discovery of histone readers with multiple domains, and overall the cross talk of PTMs that decorate histone proteins, has revealed that histone marks have synergistic roles in modulating enzyme recruitment and subsequent chromatin activities. Here, we demonstrate that the middle-down MS strategy can be combined with metabolic labeling for enhanced quantification of histone proteins and their combinatorial PTMs in a dynamic manner. We used a nanoHPLC-MS/MS system consisting of hybrid weak cation exchange-hydrophilic interaction chromatography combined with high resolution MS and MS/MS with ETD fragmentation. After spectra identification, we filtered confident hits and quantified polypeptides using our in-house software isoScale. We first verified that middle-down MS can discriminate and differentially quantify unlabeled from heavy labeled histone N-terminal tails (heavy lysine and arginine residues). Results revealed no bias toward identifying and quantifying unlabeled versus heavy labeled tails, even if the heavy labeled peptides presented the typical skewed isotopic pattern typical of long protein sequences that hardly get 100% labeling. Next, we plated epithelial cells into a media with heavy methionine-(methyl-(13)CD3), the precursor of the methyl donor S-adenosylmethionine and stimulated epithelial to mesenchymal transition (EMT). We assessed that results were reproducible across biological replicates and with data obtained using the more widely adopted bottom-up MS strategy, i.e., analysis of short tryptic peptides. We found remarkable differences in the incorporation rate of methylations in non-confluent cells versus confluent cells. Moreover, we showed that H3K27me3 was a critical player during the EMT process, as a consistent portion of histones modified as H3K27me2K36me2 in epithelial cells were converted into H3K27me3K36me2 in mesenchymal cells. We demonstrate that middle-down MS, despite being a more scarcely exploited MS technique than bottom-up, is a robust quantitative method for histone PTM characterization. In particular, middle-down MS combined with metabolic labeling is currently the only methodology available for investigating turnover of combinatorial histone PTMs in dynamic systems.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 70 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 26%
Student > Bachelor 7 10%
Researcher 7 10%
Other 6 9%
Student > Master 6 9%
Other 12 17%
Unknown 14 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 29 41%
Agricultural and Biological Sciences 11 16%
Chemistry 6 9%
Pharmacology, Toxicology and Pharmaceutical Science 4 6%
Engineering 2 3%
Other 2 3%
Unknown 16 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 January 2018.
All research outputs
#14,943,828
of 22,985,065 outputs
Outputs from Epigenetics & Chromatin
#427
of 568 outputs
Outputs of similar age
#186,772
of 313,520 outputs
Outputs of similar age from Epigenetics & Chromatin
#10
of 12 outputs
Altmetric has tracked 22,985,065 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 568 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.7. This one is in the 21st percentile – i.e., 21% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 313,520 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.