S-nitrosylation (SNO), an oxidative post-translational modification of cysteine residues, responds to changes in the cardiac redox-environment. Classic biotin switch assay and its derivatives are the most common methods used for detecting SNO. In this approach, the labile SNO group is selectively replaced with a single stable tag. To date, a variety of thiol-reactive tags have been introduced. However, these methods have not produced a consistent dataset which suggests an incomplete capture by a single tag and potentially the presence of different cysteine subpopulations.
To investigate potential labeling bias in the existing methods with a single tag to detect SNO, explore if there are distinct cysteine subpopulations, and then, develop a strategy to maximize the coverage of SNO proteome.
We obtained SNO-modified cysteine datasets for wild-type and S-nitrosoglutathione reductase (GSNOR) knock-out mouse hearts (GSNOR is a negative regulator of GSNO production) and NO-induced human embryonic kidney cell using two labeling reagents; the cysteine-reactive pyridyldithiol and iodoacetyl based tandem mass tags. Comparison revealed that <30% of the SNO-modified residues were detected by both tags, while the remaining SNO sites were only labeled by one reagent. Characterization of the two distinct subpopulations of SNO residues indicated that pyridyldithiol reagent preferentially labels cysteine residues that are more basic and hydrophobic. Based on this observation, we proposed a parallel dual labeling strategy followed by an optimized proteomics workflow. This enabled the profiling of 493 SNO-sites in GSNOR knock-out hearts.
Using a protocol comprising two tags for dual labeling maximizes overall detection of SNO by reducing the previously unrecognized labeling bias derived from different cysteine subpopulations.
Pharmacology, Toxicology and Pharmaceutical Science
4
6%
Other
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11%
Unknown
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24%
Attention Score in Context
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 27 October 2015.
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#15,091,901
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Outputs of similar age from Circulation Research
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