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When sample replicates are limited in a label-free proteomics experiment, selecting differentially regulated proteins with an assignment of statistical significance remains difficult for proteins with a single-peptide hit or a small fold-change. This paper aims to address this issue. An important component of the approach employed here is to utilize the rule of Minimum number of Permuted Significant Pairings (MPSP) to reduce false positives. The MPSP rule generates permuted sample pairings from limited analytical replicates and simply requires that a differentially regulated protein can be selected only when it is found significant in designated number of permuted sample pairings. Both a power law global error model with a signal-to-noise ratio statistic (PLGEM-STN) and a constant fold-change threshold were initially used to select differentially regulated proteins. But both methods were found not stringent enough to control the false discovery rate to 5% in this study. On the other hand, the combination of the MPSP rule with either of these two methods significantly reduces false positives with little effect on the sensitivity to select differentially regulated proteins including those with a single-peptide hit or with a <2-fold change. [ABSTRACT FROM AUTHOR]

To study the proteome response of Mycobacterium tuberculosis H37Rv to a change in iron level, iron-starved late-log-phase cells were diluted in fresh low- and high-iron media containing [15N] -labeled asparagine as the sole nitrogen source for labeling the proteins synthesized upon dilution. We determined the relative protein abundance and protein turnover in M. tuberculosis H37Rv under these two conditions. For measurements, we used a high-resolution hybrid-linear ion trap-Fourier transform mass spectrometer coupled with nanoliquid chromatography separation. While relative protein abundance analysis shows that only 5 proteins were upregulated by high iron, 24 proteins had elevated protein turnover for the cells in the high-iron medium. This suggests that protein turnover is a sensitive parameter to assess the proteome dynamics. Cluster analysis was used to explore the interconnection of protein abundance and turnover, revealing coordination of the cellular processes of protein synthesis, degradation, and secretion that determine the abundance and allocation of a protein in the cytosol and the extracellular matrix of the cells. Further potential utility of the approach is discussed. [ABSTRACT FROM AUTHOR]