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Chemical modification of transcripts with 5’ caps occurs in all organisms. Here we report a systems-level mass spectrometry-based technique, CapQuant, for quantitative analysis of the cap epitranscriptome in any organism. The method was piloted with 21 canonical caps – m7GpppN, m7GpppNm, GpppN, GpppNm, and m2,2,7GpppG – and 5 “metabolite” caps – NAD, FAD, UDP-Glc, UDP-GlcNAc, and dpCoA. Applying CapQuant to RNA from purified dengue virus,Escherichia coli, yeast, mice, and humans, we discovered four new cap structures in humans and mice (FAD, UDP-Glc, UDP-GlcNAc, and m7Gpppm6A), cell- and tissue-specific variations in cap methylation, and surprisingly high proportions of caps lacking 2’-O-methylation, such as m7Gpppm6A in mammals and m7GpppA in dengue virus, and we did not detect cap m1A/m1Am in humans. CapQuant accurately captured the preference for purine nucleotides at eukaryotic transcription start sites and the correlation between metabolite levels and metabolite caps. The mystery around cap m1A/m1Am analysis remains unresolved.

N(6)-Methyladenosine (m(6)A) represents the most prevalent internal modification on mRNA and requires a multicomponent m(6)A methyltransferase complex in mammals. How their plant counterparts determine the global m(6)A modification landscape and its molecular link to plant development remain unknown. Here we show that FKBP12 INTERACTING PROTEIN 37 KD (FIP37) is a core component of the m(6)A methyltransferase complex, which underlies control of shoot stem cell fate in Arabidopsis. The mutants lacking FIP37 exhibit massive overproliferation of shoot meristems and a transcriptome-wide loss of m(6)A RNA modifications. We further demonstrate that FIP37 mediates m(6)A RNA modification on key shoot meristem genes inversely correlated with their mRNA stability, thus confining their transcript levels to prevent shoot meristem overproliferation. Our results suggest an indispensable role of FIP37 in mediating m(6)A mRNA modification, which is required for maintaining the shoot meristem as a renewable source for continuously producing all aerial organs in plants.

Here we describe an analytical platform for systems-level quantitative analysis of modified ribonucleosides in any RNA species, with a focus on stress-induced reprogramming of tRNA as part of a system of translational control of cell stress response. This chapter emphasizes strategies and caveats for each of the seven steps of the platform workflow: (1) RNA isolation, (2) RNA purification, (3) RNA hydrolysis to individual ribonucleosides, (4) chromatographic resolution of ribonucleosides, (5) identification of the full set of modified ribonucleosides, (6) mass spectrometric quantification of ribonucleosides, (6) interrogation of ribonucleoside datasets, and (7) mapping the location of stress-sensitive modifications in individual tRNA molecules. We have focused on the critical determinants of analytical sensitivity, specificity, precision, and accuracy in an effort to ensure the most biologically meaningful data on mechanisms of translational control of cell stress response. The methods described here should find wide use in virtually any analysis involving RNA modifications.

Here we describe an analytical platform for systems-level quantitative analysis of modified ribonucleosides in any RNA species, with a focus on stress-induced reprogramming of tRNA as part of a system of translational control of cell stress response. This chapter emphasizes strategies and caveats for each of the seven steps of the platform workflow: (1) RNA isolation, (2) RNA purification, (3) RNA hydrolysis to individual ribonucleosides, (4) chromatographic resolution of ribonucleosides, (5) identification of the full set of modified ribonucleosides, (6) mass spectrometric quantification of ribonucleosides, (6) interrogation of ribonucleoside datasets, and (7) mapping the location of stress-sensitive modifications in individual tRNA molecules. We have focused on the critical determinants of analytical sensitivity, specificity, precision, and accuracy in an effort to ensure the most biologically meaningful data on mechanisms of translational control of cell stress response. The methods described here should find wide use in virtually any analysis involving RNA modifications.