Top-down identification and quantification of stable isotope labeled proteins from Aspergillus flavus using online nano-flow reversed-phase liquid chromatography coupled to a LTQ-FTICR mass spectrometer

Abstract

Online liquid chromatography-mass spectrometric (LC-MS) analysis of intact proteins (i.e., top-down proteomics) is a growing area of research in the mass spectrometry community. A major advantage of top-down MS characterization of proteins is that the information of the intact protein is retained over the vastly more common bottom-up approach that uses protease-generated peptides to search genomic databases for protein identification. Concurrent to the emergence of top-down MS characterization of proteins has been the development and implementation of the stable isotope labeling of amino acids in cell culture (SILAC) method for relative quantification of proteins by LC-MS. Herein we describe the qualitative and quantitative top-down characterization of proteins derived from SILAC-labeled Aspergillus flavus using nanoflow reversed-phase liquid chromatography directly coupled to a linear ion trap Fourier transform ion cyclotron resonance mass spectrometer (nLC-LTQ-FTICR-MS). A. flavus is a toxic filamentous fungus that significantly impacts the agricultural economy and human health. SILAC labeling improved the confidence of protein identification, and we observed 1318 unique protein masses corresponding to 659 SILAC pairs, of which 22 were confidently identified. However, we have observed some limiting issues with regard to protein quantification using top-down MS/MS analyses of SILAC-labeled proteins. The role of SILAC labeling in the presence of competing endogenously produced amino acid residues and its impact on quantification of intact species are discussed in detail.

Figure 1

Base peak chromatogram (A) of reversed-phase separation of whole cell lysates from A. flavus and (B) Extracted ion chromatograms of peaks selected to analyze column performance using the Foley-Dorsey Equation (inset) which calculates theoretical plates, N, taking into account retention time, t_R, peak width at 10% height, W_10%, and peak asymmetry, B/A.

Figure 2

Histograms showing the molecular weight distributions in 0.1 kDa bins of the predicted proteome of A. flavus (top) and the observed molecular weights from a single LC-MS experiment (bottom). The distribution of molecular weight bands of intact species is visualized on a 1D-gel (right).

Figure 3

Extracted ion chromatograms and MS spectra of SILAC pairs showing the corresponding elution of light and heavy isotopic envelopes of SILAC labeled proteins. Arginine counting and MS/MS spectra of the isolated light peak resulted in the identified sequence identified with confidence reported as an expectation value in ProSightPC.

Figure 4

Probability of complete heavy isotope incorporation into a given protein based on number of arginines at given labeling efficiencies. Using frequency of arginine in Swiss-Prot Database (~5%), number of Arginine residues are connected to a theoretical molecular weight.

Figure 5

Full Scan FTMS spectra of SILAC pairs identified in Figure 3 and modeled heavy labeled isotope distributions using Equation 2 at labeling efficiencies best fitting the raw spectrum distribution. The sum of individual modeled isotopic peaks is overlaid upon the RAW spectra (circles).