Perturbation and Interpretation of Nitrogen Isotope Distribution Patterns in Proteomics

Abstract

This study provides a discussion on the applications and limitations of (15)NH(4)(+) metabolic labeling in proteomic studies. The hyperthemophilic crenarchaeon Sulfolobus solfataricus was used as a model organism throughout this study. The distribution of nitrogen was studied in four different experiments in which this distribution was manipulated in a unique way. The experiments included full adaptation to media with relative isotope abundances (RIA) of 0.36%, 50%, and >98% (15)NH(4)(+). The incorporation efficiency was calculated on the basis of a comparison between theoretical and experimental spectra. In the case of full adaptation, incorporation efficiencies reflected the RIA (0.36%, 47.5% and 99% respectively). Labeling efficiencies were calculated on the basis of peak areas in TOF-MS spectra. It is shown that in the case of full adaptation, labeling efficiencies are 100%. In addition, we demonstrate that (15)NH(4)(+) labeling can be used in protein turnover studies, even when labeling is incomplete. In this case, incorporation efficiencies of 88-93% (lower than the RIA) were measured, providing evidence for amino acid recycling. Labeling efficiencies were always between 63% and 94% providing evidence for protein degradation. Finally, it was shown that isotope distributions can be useful in peptide identification.

Protein-SIP enables time-resolved analysis of the carbon flux in a sulfate-reducing, benzene-degrading microbial consortium

“…For each peptide, at least six single MS scans from the extracted ion chromatogram were averaged. For the calculation of 13 C RIAs (defined as the percentage of 13 C atoms in relation to the total number of carbon atoms in a peptide), a widely applied method based on comparison of theoretical and experimental spectral data was used (Snijders et al, 2005). A Pearson correlation coefficient (R) was used to determine the best fit between calculated data and experimental data as described in MacCoss et al (2005).…”

Quantitative Analysis of Isotope Distributions In Proteomic Mass Spectrometry Using Least-Squares Fourier Transform Convolution

“…Experimental data points are shown as closed circles, and the least-squares fits using eqs [13][14][15][16][17][18][19][20][21][22] Composite peaks from unlabeled and fractionally 13 C-Ile-labeled peptides resulting from pulse labeling. Experimental data points are shown as closed circles, and the least-squares fits using eqs [13][14][15][16][17][18][19][20][21][22] Composite peak from an unlabeled and fractional 13 C-Ile/ 2 H-Leu-labeled peptide resulting from pulse labeling. Experimental data points are shown as closed circles, and the least-squares fits using eqs 13-22 are shown as the solid line.…”

“…The observed spectrum can be expressed as the sum of the two isotope distributions for the two species (14) where B is a baseline offset, A U is the amplitude of the unlabeled distribution S U (m), and A L is the amplitude of the labeled distribution S L (m). The isotope distributions for the unlabeled and labeled species are readily calculated in the μ-domain: (15) ( 16) In order to calculate the labeled distribution, a new atom type, N* for 15 N-enriched atoms, is introduced for the labeled nitrogen atoms, and n elements is increased by one. The new vector of atomic μ-domain functions is given by: (17) The unlabeled part of the spectrum S U (m), is calculated using the μ-domain function f U (μ) calculated with the atomic composition vector n̂U (18) while the labeled part of the spectrum S L (m) is calculated using the μ-domain function f L (μ) calculated with the atomic composition vector n̂L: (19) The exact mass vector for N* is identical to that of N, but the abundance vector for N* contains a new variable parameter θ that represents the fractional labeling with 15 N: (20) The magnitude of the experimental error in the monoisotopic mass is expected to be on the order of the accuracy of the instrument, and for optimal peak fitting it is necessary to allow for small mass offsets due to calibration errors or limitations on accuracy.…”

Quantification of Polypeptides by Mass Spectrometry

“…Another approach for metabolic labeling that can be used for relative quantitation of polypeptides involves culturing cells in medium in which ammonia, a common source of nitrogen, is replaced by ammonia enriched in 15 N [97][98][99]. The disadvantage of this approach, when compared with the SILAC approach, is that the mass difference between heavy and light peptides depends on the number of nitrogen atoms in the polypeptide chain and it is thus not known a priori, a fact that complicates downstream analysis.…”