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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]

Understanding the biology of Mycobacterium tuberculosis is one of the primary challenges in current tuberculosis research. Investigation of mycobacterial biology using the systems biology approach has deciphered much information with regard to the bacilli and tuberculosis pathogenesis. The modulation of its environment and the ability to enter a dormant phase are the hallmarks of M. tuberculosis. Until now, proteome studies have been able to understand much about the role of various proteins, mostly in growing M. tuberculosis cells. It has been difficult to study dormant M. tuberculosis by conventional proteomic techniques with very few proteins being found to be differentially expressed. Discrepancy between proteome and transcriptome studies lead to the conclusion that a certain aspect of the mycobacterial proteome is not being explored. Analysis of protein turnover may be the answer to this dilemma. This review, while giving a gist of the proteome response of mycobacteria to various stresses, analyzes the data obtained from abundance studies versus data from protein turnover studies in M. tuberculosis. This review brings forth the point that protein turnover analysis is capable of discerning more subtle changes in protein synthesis, degradation, and secretion activities. Thus, turnover studies could be incorporated to provide a more in-depth view into the proteome, especially in dormant or persistent cells. Turnover analysis might prove helpful in drug discovery and a better understanding of the dynamic nature of the proteome of mycobacteria. [ABSTRACT FROM AUTHOR]

Background: Many studies have provided algorithms or methods to assess a statistical significance in quantitative proteomics when multiple replicates for a protein sample and a LC/MS analysis are available. But, confidence is still lacking in using datasets for a biological interpretation without protein sample replicates. Although a fold-change is a conventional threshold that can be used when there are no sample replicates, it does not provide an assessment of statistical significance such as a false discovery rate (FDR) which is an important indicator of the reliability to identify differentially expressed proteins. In this work, we investigate whether differentially expressed proteins can be detected with a statistical significance from a pair of unlabeled protein samples without replicates and with only duplicate LC/MS injections per sample. A FDR is used to gauge the statistical significance of the differentially expressed proteins. Results: We have experimented to operate on several parameters to control a FDR, including a fold-change, a statistical test, and a minimum number of permuted significant pairings. Although none of these parameters alone gives a satisfactory control of a FDR, we find that a combination of these parameters provides a very effective means to control a FDR without compromising the sensitivity. The results suggest that it is possible to perform a significance analysis without protein sample replicates. Only duplicate LC/MS injections per sample are needed. We illustrate that differentially expressed proteins can be detected with a FDR between 0 and 15% at a positive rate of 4-16%. The method is evaluated for its sensitivity and specificity by a ROC analysis, and is further validated with a [15N]-labeled internal-standard protein sample and additional unlabeled protein sample replicates. Conclusion: We demonstrate that a statistical significance can be inferred without protein sample replicates in label-free quantitative proteomics. The approach described in this study would be useful in many exploratory experiments where a sample amount or instrument time is limited. Naturally, this method is also suitable for proteomics experiments where multiple sample replicates are available. It is simple, and is complementary to other more sophisticated algorithms that are not designed for dealing with a small number of sample replicates. [ABSTRACT FROM AUTHOR]

Background: Although fold change is a commonly used criterion in quantitative proteomics for differentiating regulated proteins, it does not provide an estimation of false positive and false negative rates that is often desirable in a large-scale quantitative proteomic analysis. We explore the possibility of applying the Significance Analysis of Microarray (SAM) method (PNAS 98:5116-5121) to a differential proteomics problem of two samples with replicates. The quantitative proteomic analysis was carried out with nanoliquid chromatography/linear iron trap-Fourier transform mass spectrometry. The biological sample model included two Mycobacterium smegmatis unlabeled cell cultures grown at pH 5 and pH 7. The objective was to compare the protein relative abundance between the two unlabeled cell cultures, with an emphasis on significance analysis of protein differential expression using the SAM method. Results using the SAM method are compared with those obtained by fold change and the conventional t-test. Results: We have applied the SAM method to solve the two-sample significance analysis problem in liquid chromatography/mass spectrometry (LC/MS) based quantitative proteomics. We grew the pH5 and pH7 unlabelled cell cultures in triplicate resulting in 6 biological replicates. Each biological replicate was mixed with a common 15N-labeled reference culture cells for normalization prior to SDS/PAGE fractionation and LC/MS analysis. For each biological replicate, one center SDS/PAGE gel fraction was selected for triplicate LC/MS analysis. There were 121 proteins quantified in at least 5 of the 6 biological replicates. Of these 121 proteins, 106 were significant in differential expression by the t-test (p < 0.05) based on peptide-level replicates, 54 were significant in differential expression by SAM with Δ = 0.68 cutoff and false positive rate at 5%, and 29 were significant in differential expression by the t-test (p < 0.05) based on protein-level replicates. The results indicate that SAM appears to overcome the false positives one encounters using the peptide-based t-test while allowing for identification of a greater number of differentially expressed proteins than the protein-based t-test. Conclusion: We demonstrate that the SAM method can be adapted for effective significance analysis of proteomic data. It provides much richer information about the protein differential expression profiles and is particularly useful in the estimation of false discovery rates and miss rates. [ABSTRACT FROM AUTHOR]

We determined the global protein turnover profiles for Mycobacterium smegmatis under acid shock and iron starvation conditions using a simple 15N isotope doping technique and a complete medium replacement method for chasing. We used a high-resolution hybrid-linear ion trap-Fourier transform mass spectrometer coupled with nanoliquid chromatography separation to measure protein turnover values for 151 proteins over a dynamic range of 3 orders of magnitude ranging from about 0.2 to 500. Of these 151 proteins, 31 had significant protein turnover changes (p <0.05) at both stress conditions and had protein turnover values increased or decreased by more than 2-fold under at least one stress condition. Protein turnover increased under acid shock for 28 of the 31 proteins but decreased under iron starvation for all the 31 proteins. Only two proteins had protein turnover lowered by more than 2-fold (p <0.05) under both stress conditions, including an ATP synthase F1 β subunit (MSMEG4921; AtpD) and a catalase/peroxidase (MSMEG6346; KatG). KatG is required for in vivo activation of isoniazid to be bacterialcidal. Decrease of KatG protein turnover under both stress conditions supports the view that isoniazid may induce a dormancy program in mycobacteria, which in turn limits the efficacy of this drug against dormant subpopulation of mycobacteria. Thus, measuring protein turnover in stressed Mycobacterium cells has implications in understanding drug action and resistance mechanisms. [ABSTRACT FROM AUTHOR]