Author(s): Gao J, Opiteck GJ, Friedrichs MS, Dongre AR, Hefta SA
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Abstract Protein expression trends in yeast were monitored as a function of carbon source (glucose versus galactose) using multidimensional high performance liquid chromatography (HPLC) coupled to gas-phase fractionation, using relative intensity triggering (GPFri). Size exclusion HPLC was used to separate whole cell lysates, and following proteolysis of these fractions, each was separated by reversed phase HPLC, which was coupled on-line via electrospray to an ion trap mass spectrometer. The GPFri technique increased the dynamic range of proteins detected by increasing the number of peptide ions subjected to low energy collision induced dissociation to the 24 most intense ions in each of the survey scans. No protein or peptide labeling was used; instead, the number of SEQUEST identifications for each peptide (previously termed "hits") were used as a semiquantitative means of assessing both the direction (increase vs decrease) and significance of change in protein abundance. None of the traditional SEQUEST filters, e.g., Xcorr, DelCn, Sp, Rsp, etc., were employed in this study. Instead, a Student's t-test was used to distinguish those proteins that significantly and reproducibly changed between carbon sources from those that did not. This relied on the SEQUEST misassignments occurring in equal proportion between treatments and thereby negating each other; statistically significant changes in SEQUEST assignments were nonrandom events by definition and therefore reflective of correct identifications. This method of data analysis showed a large degree of concordance with results reported by other groups in similar transcriptional profiling and proteomic experiments. In all, 176 and 231 (fold-change > or = 1.1; p < or = 0.05) proteins were identified as being increased in peptide hit number when the yeast cells' source of carbon was changed between glucose and galactose, respectively.
This article was published in J Proteome Res
and referenced in Journal of Proteomics & Bioinformatics