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Bacteria-mediated immunomodulation has important implications in microbial infection and bacterial vaccines. Intraphagosomal bacteria negotiate survival niches with the intracellular environment by modulating the phagosome composition during invasion. The final phagosome composition determines the fate of the intraphagosomal bacterium or the efficacy of a bacterial vaccine. Therefore, the phagosome proteome is a valuable readout to assess the ability of a natural or genetically engineered bacterial strain to modulate the host immune response. Compared to a preparation of latex-bead-containing phagosomes, the preparation of bacterial phagosomes requires additional measures to ensure comparable purity due to their closer density to some other organelles. This bottleneck can be overcome with delicate preparation protocols, proper experimental designs to facilitate bioinformatics based discrimination against contaminating proteins, and the incorporation of stable-isotope labeled internal standards to correct for contaminating fractions of phagosomal proteins. The rapid progress in the proteomics and bioinformatics fields provides an array of techniques that promise to bring about an unprecedented coverage of both known and as yet undiscovered immunomodulation pathways within bacterial phagosome proteomes. A precise portrait of the bacteria-mediated immunomodulation pathways in phagosomes will likely aid in the intelligent design of bioengineered bacterial vaccine strains for important future biomedical applications.

Phagosomal proteome characterization has contributed significantly to the understanding of host–pathogen interaction and the mechanism of infectious diseases caused by intracellular bacteria. The latex bead-containing phagosome has been widely used as a model system to study phagosomal proteomes at a global level. In contrast, the study of bacteria-containing phagosomes at a similar level has just begun. A number of intracellular microbial species are studied for their proteomes during the invasion of a host, providing insight into their metabolic adaptation in host cells and interaction with host-cell antimicrobial environments. In this review, we attempt to summarize the most recent advancements in the proteomic study of microbial phagosomes, especially those originating from mouse or human cells. We also briefly describe the proteomics of latex bead-containing phagosomes because they are often used as model phagosomes for study. We provide descriptions on major biological and technological components in phagosomal proteome studies. We also discuss the role of phagosomal proteome study in the broader horizon of systems biology and the technological challenges in phagosomal proteome characterization.

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.

Methanosarcina acetivorans strain C2A is an acetate- and methanol-utilizing methane-producing organism for which the genome, the largest yet sequenced among the Archaea, reveals extensive physiological diversity. LC linear ion trap-FTICR mass spectrometry was employed to analyze acetate- vs. methanol-grown cells metabolically labeled with 14N vs. 15N, respectively, to obtain quantitative protein abundance ratios. DNA microarray analyses of acetate- vs. methanol-grown cells was also performed to determine gene expression ratios. The combined approaches were highly complementary, extending the physiological understanding of growth and methanogenesis. Of the 1081 proteins detected, 255 were ≥ 3-fold differentially abundant. DNA microarray analysis revealed 410 genes that were ≥ 2.5-fold differentially expressed of 1972 genes with detected expression. The ratios of differentially abundant proteins were in good agreement with expression ratios of the encoding genes. Taken together, the results suggest several novel roles for electron transport components specific to acetate-grown cells, including two flavodoxins each specific for growth on acetate or methanol. Protein abundance ratios indicated that duplicate CO dehydrogenase/acetyl-CoA complexes function in the conversion of acetate to methane. Surprisingly, the protein abundance and gene expression ratios indicated a general stress response in acetate- vs. methanol-grown cells that included enzymes specific for polyphosphate accumulation and oxidative stress. The microarray analysis identified transcripts of several genes encoding regulatory proteins with identity to the PhoU, MarR, GlnK, and TetR families commonly found in the Bacteria domain. An analysis of neighboring genes suggested roles in controlling phosphate metabolism (PhoU), ammonia assimilation (GlnK), and molybdopterin cofactor biosynthesis (TetR). Finally, the proteomic and microarray results suggested roles for two-component regulatory systems specific for each growth substrate.

Methanosarcina acetivorans produces acetate, formate, and methane when cultured with CO as the growth substrate [Rother M, Metcalf WW (2004) Proc Natl Acad Sci USA 101: 16929–16934 ], which suggests novel features of CO metabolism. Here we present a genome-wide proteomic approach to identify and quantify proteins differentially abundant in response to growth on CO versus methanol or acetate. The results indicate that oxidation of CO to CO 2 supplies electrons for reduction of CO 2 to a methyl group by steps and enzymes of the pathway for CO 2 reduction determined for other methane-producing species. However, proteomic and quantitative RT-PCR results suggest that reduction of the methyl group to methane involves novel methyltransferases and a coenzyme F 420 H 2 :heterodisulfide oxidoreductase system that generates a proton gradient for ATP synthesis not previously described for pathways reducing CO 2 to methane. Biochemical assays support a role for the oxidoreductase, and transcriptional mapping identified an unusual operon structure encoding the oxidoreductase. The proteomic results further indicate that acetate is synthesized from the methyl group and CO by a reversal of initial steps in the pathway for conversion of acetate to methane that yields ATP by substrate level phosphorylation. The results indicate that M. acetivorans utilizes a pathway distinct from all known CO 2 reduction pathways for methane formation that reflects an adaptation to the marine environment. Finally, the pathway supports the basis for a recently proposed primitive CO-dependent energy-conservation cycle that drove and directed the early evolution of life on Earth.

A new algorithm (QN) for the (15)N /(14)N quantitation of relative protein abundances in complex proteomic samples is described. QN takes advantage of the high resolution, mass accuracy and throughput of the hybrid mass spectrometer LTQ-FT MS. Peptide quantitation is based on MS peak intensity (measured in the FT MS), while peptide identification is performed in the MS/MS mode (measured in the LTQ linear ion trap). Accuracy of the protein abundance is enhanced by a novel scoring procedure, allowing filtering of less reliable measurements of peptide abundances. The performance of QN is illustrated in the relative quantitative analysis of M. acetivorans C2A cultures grown with carbon monoxide vs methanol as substrate. Roughly 1,000 proteins were quantitated with an average CV of 9% for the protein abundance ratios. QN performs quantitation without manual intervention, does not require high processing power, and generates files compatible with the Guidelines for Proteomic Data Publication.

The Fourier-transform mass spectrometry (FTMS) instrument offers a mass resolution higher than most of other mass spectrometers. This high resolution is in part due to the better stability offered by a superconductor magnet in FTMS than a radio frequency voltage utilized in many other mass spectrometers. The extremely high resolution of FTMS has very important application in biomedical proteomics research. The high resolution not only dramatically improves the reliability of protein identification but also the accuracy of protein quantitation. In this chapter, we present several examples of proteomics study that takes advantage of the high resolution offered by FTMS. Particularly, we describe examples of proteome dynamic study with isotopomer analysis, and precise peptide and protein label-free quantitation with rigorous statistical assessments. In protein dynamic studies, FTMS readily resolves all of the isotopomer peaks for peptides. The well-resolved isotopomer peaks allow a direct integration of the intensities of different isotopologue envelopes without the need of a deconvolution algorithm. With the highresolution data, we were able to show that protein turnover measurement revealed more subtle changes in the dynamics of a proteome. The high resolution of FTMS helps to reduce the interference of contaminant peaks (Fig. 1). The ability to resolve the targeted peptide isotopomer peaks from interfering ones greatly facilitates the implementation of a label-free quantitative proteomics method that relies on peptide cross reference between liquid chromatography runs and the integration of extracted ion chromatographic intensities (Lipton et al., 2002). With both high confidence in peptide identification and quantitation, we showed that the major source of variability lies more in sample preparation than in liquid chromatography/mass spectrometry analysis. Such a result has a direct consequence in the statistical approaches utilized to assign significance in label-free quantitative proteomics. Two sections are presented in this chapter. The first one briefly discusses the general aspect of a protein turnover analysis followed by examples of proteome dynamics study in acid stressed and iron limited mycobacterial cells. The second one describes the utilization of high-resolution FTMS data for label-free quantitation of proteins with a rigorous statistical assessment of significance in differential protein abundances. The spectra shown in the panels a to e of Fig. 1 are for a tryptic peptide from a superoxide dismutase in M. smegmatis (MSMEG_6427; Mn) with a sequence of AFWNVVNWDDVQNR.

Proteomics has been applied to study intracellular bacteria and phagocytic vacuoles in different host cell lines, especially macrophages (Mφs). For mycobacterial phagosomes, few studies have identified over several hundred proteins for systems assessment of the phagosome maturation and antigen presentation pathways. More importantly, there has been a scarcity in publication on proteomic characterization of mycobacterial phagosomes in dendritic cells (DCs). In this work, we report a global proteomic analysis of Mφ and DC phagosomes infected with a virulent, an attenuated, and a vaccine strain of mycobacteria. We used label-free quantitative proteomics and bioinformatics tools to decipher the regulation of phagosome maturation and antigen presentation pathways in Mφs and DCs. We found that the phagosomal antigen presentation pathways are repressed more in DCs than in Mφs. The results suggest that virulent mycobacteria might co-opt the host immune system to stimulate granuloma formation for persistence while minimizing the antimicrobial immune response to enhance mycobacterial survival. The studies on phagosomal proteomes have also shown promise in discovering new antigen presentation mechanisms that a professional antigen presentation cell might use to overcome the mycobacterial blockade of conventional antigen presentation pathways.

Selecting differentially regulated proteins with an assignment of statistical significance remains difficult for proteins with a single-peptide hit or a small fold-change when sample replicates are limited. This study presents a label-free quantitative proteomics scheme that was used to select differentially regulated proteins with single-peptide hits and with2-fold change at a 5% false discovery rate. The scheme incorporated a labeled internal control into two unlabeled samples to facilitate error modeling when there were no replicates for the unlabeled samples. The results showed that, while both a power law global error model with a signal-to-noise ratio statistic (PLGEM-STN) and a constant fold-change threshold could be used, neither of them alone was stringent enough to select differentially regulated proteins at a 5% false discovery rate. Thus, the rule of minimum number of permuted significant pairings (MPSP) was introduced to reduce false discovery rates in combination with PLGEM-STN or a fold-change threshold. MPSP played a critical role in extending the selection of differentially regulated proteins to those with single-peptide hits or with lower fold-changes. Although the approaches were demonstrated for limited sample replicates, they should also be applicable to the situation where more sample replicates are available.

The concept of a dynamic state of body constituents, a precursor of the modern term of proteome dynamics, was conceived over a century ago. But, not until recently can we examine the dynamics of individual "constituents" for example, proteins at a truly global level. The path of advancement in our understanding of protein turnover at the global level is marked by the introduction of some key technological innovations. These methods include the isotopic tracer technique in the 1930s, the two-dimensional gel electrophoresis technique in the 1970s, the sector mass spectrometer that could analyze isotopomers of peptides in the early 1990s, the 2D gel/MALDI-TOF proteomics technology in the late 1990s, the booming liquid chromatography/mass spectrometry proteomics technology in this decade, and the recently emerging protein-tagging approaches that offer single-cell resolution for protein turnover measurements. The long-standing inquiry raised in the 1950s about the existence of a dynamic state in different organisms at different physiological conditions can now be answered with an individual "constituent" resolution on a truly global scale. Now it appears that protein degradation is not necessarily an end to the protein function. Rather, it can be the start of a new function because protein degradation clears the way for the action of other proteins. Protein turnover participates in a multi-layer complex regulatory network and shares equal importance with gene transcription and protein translation. The advances in technologies for protein turnover analysis and the improved understanding of the biological role of protein turnover will likely help to solve some long-standing biomedical problems such as the tuberculosis disease that at the present day still affects one-third of the world population.

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.

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.