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Deciphering the “m6A Code” via Antibody-Independent Quantitative Profiling
Author(s) -
Miguel Angel García-Campos,
Sarit Edelheit,
Ursula Toth,
Modi Safra,
Ran Shachar,
Sergey Viukov,
Roni Winkler,
Ronit Nir,
Lior Lasman,
Alexander Brandis,
Jacob H. Hanna,
Walter Rossmanith,
Schraga Schwartz
Publication year - 2019
Publication title -
cell
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 26.304
H-Index - 776
eISSN - 1097-4172
pISSN - 0092-8674
DOI - 10.1016/j.cell.2019.06.013
Subject(s) - biology , profiling (computer programming) , computational biology , antibody , genetics , evolutionary biology , programming language , computer science
N6-methyladenosine (m 6 A) is the most abundant modification on mRNA and is implicated in critical roles in development, physiology, and disease. A major limitation has been the inability to quantify m 6 A stoichiometry and the lack of antibody-independent methodologies for interrogating m 6 A. Here, we develop MAZTER-seq for systematic quantitative profiling of m6A at single-nucleotide resolution at 16%-25% of expressed sites, building on differential cleavage by an RNase. MAZTER-seq permits validation and de novo discovery of m 6 A sites, calibration of the performance of antibody-based approaches, and quantitative tracking of m 6 A dynamics in yeast gametogenesis and mammalian differentiation. We discover that m6A stoichiometry is "hard coded" in cis via a simple and predictable code, accounting for 33%-46% of the variability in methylation levels and allowing accurate prediction of m 6 A loss and acquisition events across evolution. MAZTER-seq allows quantitative investigation of m 6 A regulation in subcellular fractions, diverse cell types, and disease states.

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