Mass spectrometry of the M. smegmatis proteome: protein expression levels correlate with function, operons, and codon bias

The physiological effect of rimI/rimJ silencing by CRISPR interference in Mycobacterium smegmatis mc2155

N-terminal acetylation of proteins is an important post-translational modification (PTM) found in eukaryotes and prokaryotes. In bacteria, N-terminal acetylation is suggested to play various regulatory roles related to protein stability, gene expression, stress response, and virulence; however, the mechanism of such response remains unclear. The proteins, namely RimI/RimJ, are involved in N-terminal acetylation in mycobacteria. In this study, we used CRISPR interference (CRISPRi) to silence rimI/rimJ in Mycobacterium smegmatis mc²155 to investigate the physiological effects of N-terminal acetylation in cell survival and stress response. Repeat analysis of growth curves in rich media and biofilm analysis in minimal media of various mutant strains and wild-type bacteria did not show significant differences that could be attributed to the rimI/rimJ silencing. However, total proteome and acetylome profiles varied significantly across mutants and wild-type strains, highlighting the role of RimI/RimJ in modulating levels of proprotein acetylation in the cellular milieu. Further, we observed a significant increase in the minimum inhibitory concentration (MIC) (from 64 to 1024 µg ml^-1) for the drug isoniazid in rimI mutant strains. The increase in MIC value for the drug isoniazid in the mutant strains suggests the link between N-terminal acetylation and antibiotic resistance. The study highlights the utility of CRISPRi as a convenient tool to study the role of PTMs, such as acetylation in mycobacteria. It also identifies rimI/rimJ genes as necessary for managing cellular response against antibiotic stress. Further research would be required to decipher the potential of targeting acetylation to enhance the efficacy of existing antibiotics.

Immunomodulatory Effects of Recombinant Mycobacterium smegmatis Expressing Antigen-85B Epitopes in Infected J774A.1 Murine Macrophages

Tuberculosis (TB) causes more than 1.5 million deaths each year, remaining a significant global health problem. Mycobacterium smegmatis (M. smegmatis) and Mycobacterium tuberculosis (M. tuberculosis) share features, which support the use of the former use in new generation TB vaccine development. In a previous study, the specific humoral and cellular immunogenicity of a recombinant M. smegmatis strain expressing epitopes from M. tuberculosis Ag85B protein (rMs064), was demonstrated in mice. In the current study, the immunomodulatory capacity of rMs064 was determined in a J774A.1 murine macrophage cell line. To determine the immunomodulatory effect of rMs064 in J774A.1 macrophages, the expression of inducible nitric oxide synthase (iNOS) and production of nitric oxide (NO) was evaluated. The expression of activation surface markers (MHC-II, CD40, CD80 and CD86) and the production of cytokines (IL-1β, TNF-α, IL-12p70 and IL-6) was also determined in rMs064 infected J774A.1 macrophages. Our findings showed the ability of rMs064 to induce substantial increases in macrophage activation markers expression; MHC class II and CD40, compared with M. smegmatis transformed with the empty vector (rMs012) and uninfected cells. rMs064 induced significant increases in IL-12p70 compared to uninfected cells. The expression of iNOS and CD86, and the production of IL-1β, and TNF-α were increased in rMs064 and rMs012, compared to uninfected cells. rMs064 demonstrated its immunomodulatory ability by stimulating the innate immune response, which supports its further evaluation as a TB vaccine candidate.

Method of extraction and proteome profiling of mycobacteria using liquid chromatography-high resolution mass spectrometry

Advances in massively parallel sequencing, of complete bacterial genomes, have led to many novel findings in the field of genomics. However, these data often lack correlation with expressed protein profiles. It has been demonstrated that even very closely related genomes, such as in mycobacteria, express drastically different phenotypes. These phenotypes often have major roles in pathogenicity. Therefore, it is just as important to have a method for examining the proteome of a bacterium as well as its genome. These studies are further complicated in mycobacteria due to the cell wall and mycolic acid. A comprehensive method for the identification and characterization of the whole mycobacterium protein profile is needed. In the present study, a simple, sensitive, and specific liquid chromatography tandem mass spectrometry method was developed for the extraction, purification and profiling the mycobacterial proteome in various species. During development, sonication and bead-beating cell lysis protocol was tested using 15% Acetonitrile and 6 M guanidine-HCl (GuHCl) as extraction solvent. Sonication lysis in 6 M GuHCl with glass beads was the preferred method for cell lysis. This method was developed using reverse phase liquid chromatography and a Q Exactive ™ Plus Orbitrap™ mass spectrometer for peptide and protein identification. Bottom-up liquid chromatography-mass spectrometry LC–MS analysis resulted in identification of greater than 2500 proteins.

Metabolomics Studies To Decipher Stress Responses in Mycobacterium smegmatis Point to a Putative Pathway of Methylated Amine Biosynthesis

Mycobacterium smegmatis, the saprophytic soil mycobacterium, is routinely used as a surrogate system to study the human pathogen Mycobacterium tuberculosis It has also been reported as an opportunistic pathogen in immunocompromised hosts. In addition, it can exist in several ecological setups, thereby suggesting its capacity to adapt to a variety of environmental cues. In this study, we employed untargeted proton nuclear magnetic resonance (¹H-NMR)-based metabolomics to identify metabolites and metabolic pathways critical for early adaptive responses to acidic stress, oxidative stress, and nutrient starvation in Mycobacterium smegmatis We identified 31, 20, and 46 metabolites that showed significant changes in levels in response to acidic, oxidative, and nutrient starvation stresses, respectively. Pathway analyses showed significant perturbations in purine-pyrimidine, amino-acid, nicotinate-nicotinamide, and energy metabolism pathways. Besides these, differential levels of intermediary metabolites involved in α-glucan biosynthesis pathway were observed. We also detected high levels of organic osmolytes, methylamine, and betaine during nutrient starvation and oxidative stress. Further, tracing the differential levels of these osmolytes through computational search tools, gene expression studies (using reverse transcription-PCR [RT-PCR]), and enzyme assays, we detected the presence of a putative pathway of biosynthesis of betaine, methylamine, and dimethylamine previously unreported in Mycobacterium smegmatis IMPORTANCE Alterations in metabolite levels provide fast and direct means to regulate enzymatic reactions and, therefore, metabolic pathways. This study documents, for the first time, the metabolic changes that occur in Mycobacterium smegmatis as a response to three stresses, namely, acidic stress, oxidative stress, and nutrient starvation. These stresses are also faced by intracellular mycobacteria during infection and therefore may be extended to frame therapeutic interventions for pathogenic mycobacteria. In addition to the purine-pyrimidine, amino acid, nicotinate-nicotinamide, and energy metabolism pathways that were found to be affected in response to different stresses, a novel putative methylamine biosynthesis pathway was identified to be present in Mycobacterium smegmatis.

Metabolite analysis of Mycobacterium species under aerobic and hypoxic conditions reveals common metabolic traits

A metabolite profiling approach has been implemented to elucidate metabolic adaptation at set culture conditions in five Mycobacterium species (two fast- and three slow-growing) with the potential to act as model organisms for Mycobacterium tuberculosis (Mtb). Analysis has been performed over designated growth phases and under representative environments (nutrient and oxygen depletion) experienced by Mtb during infection. The procedure was useful in determining a range of metabolites (60-120 compounds) covering nucleotides, amino acids, organic acids, saccharides, fatty acids, glycerols, -esters, -phosphates and isoprenoids. Among these classes of compounds, key biomarker metabolites, which can act as indicators of pathway/process activity, were identified. In numerous cases, common metabolite traits were observed for all five species across the experimental conditions (e.g. uracil indicating DNA repair). Amino acid content, especially glutamic acid, highlighted the different properties between the fast- and slow-growing mycobacteria studied (e.g. nitrogen assimilation). The greatest similarities in metabolite composition between fast- and slow-growing mycobacteria were apparent under hypoxic conditions. A comparison to previously reported transcriptomic data revealed a strong correlation between changes in transcription and metabolite content. Collectively, these data validate the changes in the transcription at the metabolite level, suggesting transcription exists as one of the predominant modes of cellular regulation in Mycobacterium. Sectors with restricted correlation between metabolites and transcription (e.g. hypoxic cultivation) warrant further study to elucidate and exploit post-transcriptional modes of regulation. The strong correlation between the laboratory conditions used and data derived from in vivo conditions, indicate that the approach applied is a valuable addition to our understanding of cell regulation in these Mycobacterium species.

Proteogenomic Analysis of Mycobacterium smegmatis Using High Resolution Mass Spectrometry

Biochemical evidence is vital for accurate genome annotation. The integration of experimental data collected at the proteome level using high resolution mass spectrometry allows for improvements in genome annotation by providing evidence for novel gene models, while validating or modifying others. Here, we report the results of a proteogenomic analysis of a reference strain of Mycobacterium smegmatis (mc²155), a fast growing model organism for the pathogenic Mycobacterium tuberculosis—the causative agent for Tuberculosis. By integrating high throughput LC/MS/MS proteomic data with genomic six frame translation and ab initio gene prediction databases, a total of 2887 ORFs were identified, including 2810 ORFs annotated to a Reference protein, and 63 ORFs not previously annotated to a Reference protein. Further, the translational start site (TSS) was validated for 558 Reference proteome gene models, while upstream translational evidence was identified for 81. In addition, N-terminus derived peptide identifications allowed for downstream TSS modification of a further 24 gene models. We validated the existence of six previously described interrupted coding sequences at the peptide level, and provide evidence for four novel frameshift positions. Analysis of peptide posterior error probability (PEP) scores indicates high-confidence novel peptide identifications and shows that the genome of M. smegmatis mc²155 is not yet fully annotated. Data are available via ProteomeXchange with identifier PXD003500.

Operon Gene Order Is Optimized for Ordered Protein Complex Assembly

The assembly of heteromeric protein complexes is an inherently stochastic process in which multiple genes are expressed separately into proteins, which must then somehow find each other within the cell. Here, we considered one of the ways by which prokaryotic organisms have attempted to maximize the efficiency of protein complex assembly: the organization of subunit-encoding genes into operons. Using structure-based assembly predictions, we show that operon gene order has been optimized to match the order in which protein subunits assemble. Exceptions to this are almost entirely highly expressed proteins for which assembly is less stochastic and for which precisely ordered translation offers less benefit. Overall, these results show that ordered protein complex assembly pathways are of significant biological importance and represent a major evolutionary constraint on operon gene organization.

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Operon-encoded subunits tend to be encoded by neighboring genes and form large interfaces

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Operon gene order is often optimized for the order of protein complex assembly

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Exceptions are mostly highly expressed proteins for which assembly is less stochastic

Transcriptomic Characterization of an Infection of Mycobacterium smegmatis by the Cluster A4 Mycobacteriophage Kampy

The mycobacteriophages, phages that infect the genus Mycobacterium, display profound genetic diversity and widespread geographical distribution, and possess significant medical and ecological importance. However, most of the majority of functions of mycobacteriophage proteins and the identity of most genetic regulatory elements remain unknown. We characterized the gene expression profile of Kampy, a cluster A4 mycobacteriophage, during infection of its host, Mycobacterium smegmatis, using RNA-Seq and mass spectrometry. We show that mycobacteriophage Kampy transcription occurs in roughly two phases, an early phase consisting of genes for metabolism, DNA synthesis, and gene regulation, and a late phase consisting of structural genes and lysis genes. Additionally, we identify the earliest genes transcribed during infection, along with several other possible regulatory units not obvious from inspection of Kampy's genomic structure. The transcriptional profile of Kampy appears similar to that of mycobacteriophage TM4 but unlike that of mycobacteriophage Giles, a result which further expands our understanding of the diversity of mycobacteriophage gene expression programs during infection.

Proteogenomics in microbiology: taking the right turn at the junction of genomics and proteomics

High-accuracy and high-throughput proteomic methods have completely changed the way we can identify and characterize proteins. MS-based proteomics can now provide a unique supplement to genomic data and add a new level of information to the interpretation of genomic sequences. Proteomics-driven genome annotation has become especially relevant in microbiology where genomes are sequenced on a daily basis and limitations of an in silico driven annotation process are well recognized. In this review paper, we outline different strategies on how one can design a proteogenomic experiment, for example on genome-sequenced (synonymous proteogenomics) versus unsequenced organisms (ortho-proteogenomics) or with the aid of other "omic" data such as RNA-seq. We touch upon many challenges that are encountered during a typical proteogenomic study, mostly concerning bioinformatics methods and downstream data analysis, but also related to creation and use of sequence databases. A large list of proteogenomic case studies of different microorganisms is provided to illustrate the mapping of MS/MS-derived peptide spectra to genomic DNA sequences. These investigations have led to accurate determination of translational initiation sites, pointed out eventual read-throughs or programmed frameshifts, detected signal peptide processing or other protein maturation events, removed questionable annotation assignments, and provided evidence for predicted hypothetical proteins.

Identifying feasible metabolic routes in Mycobacterium smegmatis and possible alterations under diverse nutrient conditions

Background

Many studies on M. tuberculosis have emerged from using M. smegmatis MC²155 (Msm), since they share significant similarities and yet Msm is non-pathogenic and faster growing. Although several individual molecules have been studied from Msm, many questions remain open about its metabolism as a whole and its capability to be versatile. Adaptability and versatility are emergent properties of a system, warranting a molecular systems perspective to understand them.

Results

We identify feasible metabolic pathways in Msm in reference condition with transcriptome, phenotypic microarray, along with functional annotation of the genome. Together with transcriptome data, specific genes from a set of alternatives have been mapped onto different pathways. About 257 metabolic pathways can be considered to be feasible in Msm. Next, we probe cellular metabolism with an array of alternative carbon and nitrogen sources and identify those that are utilized and favour growth as well as those that do not support growth. In all, about 135 points in the entire metabolic map are probed. Analyzing growth patterns under these conditions, lead us to hypothesize different pathways that can become active in various conditions and possible alternate routes that may be induced, thus explaining the observed physiological adaptations.

Conclusions

The study provides the first detailed analysis of feasible pathways towards adaptability. We obtain mechanistic insights that explain observed phenotypic behaviour by studying gene-expression profiles and pathways inferred from the genome sequence. Comparison of transcriptome and phenome analysis of Msm and Mtb provides a rationale for understanding commonalities in metabolic adaptability.

Electronic supplementary material

The online version of this article (doi:10.1186/s12866-014-0276-5) contains supplementary material, which is available to authorized users.

scnRCA: a novel method to detect consistent patterns of translational selection in mutationally-biased genomes

Codon usage bias (CUB) results from the complex interplay between translational selection and mutational biases. Current methods for CUB analysis apply heuristics to integrate both components, limiting the depth and scope of CUB analysis as a technique to probe into the evolution and optimization of protein-coding genes. Here we introduce a self-consistent CUB index (scnRCA) that incorporates implicit correction for mutational biases, facilitating exploration of the translational selection component of CUB. We validate this technique using gene expression data and we apply it to a detailed analysis of CUB in the Pseudomonadales. Our results illustrate how the selective enrichment of specific codons among highly expressed genes is preserved in the context of genome-wide shifts in codon frequencies, and how the balance between mutational and translational biases leads to varying definitions of codon optimality. We extend this analysis to other moderate and fast growing bacteria and we provide unified support for the hypothesis that C- and A-ending codons of two-box amino acids, and the U-ending codons of four-box amino acids, are systematically enriched among highly expressed genes across bacteria. The use of an unbiased estimator of CUB allows us to report for the first time that the signature of translational selection is strongly conserved in the Pseudomonadales in spite of drastic changes in genome composition, and extends well beyond the core set of highly optimized genes in each genome. We generalize these results to other moderate and fast growing bacteria, hinting at selection for a universal pattern of gene expression that is conserved and detectable in conserved patterns of codon usage bias.

Deep coverage of the Escherichia coli proteome enables the assessment of false discovery rates in simple proteogenomic experiments

Recent advances in mass spectrometry (MS) have led to increased applications of shotgun proteomics to the refinement of genome annotation. The typical "proteo-genomic" workflows rely on the mapping of peptide MS/MS spectra onto databases derived via six-frame translation of the genome sequence. These databases contain a large proportion of spurious protein sequences which make the statistical confidence of the resulting peptide spectrum matches difficult to assess. Here we performed a comprehensive analysis of the Escherichia coli proteome using LTQ-Orbitrap MS and mapped the corresponding MS/MS spectra onto a six-frame translation of the E. coli genome. We hypothesized that the protein-coding part of the E. coli genome approaches complete annotation and that the majority of six frame-specific (novel) peptide spectrum matches can be considered as false positive identifications. We confirm our hypothesis by showing that the posterior error probability distribution of novel hits is almost identical to that of reversed (decoy) hits; this enables us to estimate the sensitivity, specificity, accuracy, and false discovery rate in a typical bacterial proteo-genomic dataset. We use two complementary computational frameworks for processing and statistical assessment of MS/MS data: MaxQuant and Trans-Proteomic Pipeline. We show that MaxQuant achieves a more sensitive six-frame database search with an acceptable false discovery rate and is therefore well suited for global genome reannotation applications, whereas the Trans-Proteomic Pipeline achieves higher specificity and is well suited for high-confidence validation. The use of a small and well-annotated bacterial genome enables us to address genome coverage achieved in state-of-the-art bacterial proteomics: identified peptide sequences mapped to all expressed E. coli proteins but covered 31.7% of the protein-coding genome sequence. Our results show that false discovery rates can be substantially underestimated even in "simple" proteo-genomic experiments obtained by means of high-accuracy MS and point to the necessity of further improvements concerning the coverage of peptide sequences by MS-based methods.

Contributions of speed and accuracy to translational selection in bacteria

Among bacteria, we have previously shown that species that are capable of rapid growth have stronger selection on codon usage than slow growing species, and possess higher numbers of rRNA and tRNA genes. This suggests that fast-growers are adapted for fast protein synthesis. There is also considerable evidence that codon usage is influenced by accuracy of translation, and some authors have argued that accuracy is more important than speed. Here we compare the strength of the two effects by studying the codon usages in high and low expression genes and on conserved and variable sites within high expression genes. We introduce a simple statistical method that can be used to assess the significance and the strength of the two types of bias in the same sets of sequences. We compare our statistical measure of codon bias to the common used codon adaptation index, and show that the new measure is preferable for three reasons for the purposes of this analysis. Across a large sample of bacterial genomes, both effects from speed and accuracy are clearly visible, although the speed effect appears to be much stronger than the accuracy effect and is found to be significant in a larger proportion of genomes. It is also difficult to explain the correlation of codon bias in the high expression genes with growth rates and numbers of copies of tRNA and rRNA genes on the basis of selection for accuracy. Hence we conclude that selection for translational speed is a dominant effect in driving codon usage bias in fast-growing bacteria, with selection for accuracy playing a small supplementary role.

Proteoliposomes from Mycobacterium smegmatis induce immune cross-reactivity against Mycobacterium tuberculosis antigens in mice

Proteoliposomes (PL) obtained from Mycobacterium smegmatis (Ms) were evaluated for their capacity to elicit cross-reactive responses against Mycobacterium tuberculosis (Mtb) antigens in BALB/c mice. Animals immunized with PL adjuvanted with alum (PL-AL) or Freund's Incomplete Adjuvant (PL-IFA) showed significant IgG responses against the PL as well as total Ms lipids. Both groups of animals also showed significant IgG responses against BCG, but only animals immunized with PL-AL produced significant IgG responses against soluble cell wall proteins (SCWP) or whole cell lysate (WCL) of Mtb. Significant DTH responses against WCL were observed in both groups of animals after 24 h, but only PL-AL-immunized mice showed significant DTH responses after 48 h and 72 h. PL-Ms are capable of eliciting cross-reactive humoral and cellular responses against Mtb antigens and thus may be a potential vaccine strategy against tuberculosis.

MassUntangler: a novel alignment tool for label-free liquid chromatography-mass spectrometry proteomic data

Liquid chromatography-mass spectrometry (LC-MS) has become an important analytical tool for quantitative proteomics and biomarker discovery. In the label-free differential LC-MS approach computational methods are required for an accurate alignment of peaks extrapolated from the experimental raw data accounting for retention time and m/z signals intensity, which are strongly affected by sample matrix and instrumental performance. A novel procedure "MassUntangler" for pairwise alignment has been developed, relying on a pattern-based matching algorithm integrated with filtering algorithms in a multi-step approach. The procedure has been optimized employing a two-step approach. Firstly, low-complexity LC-MS data derived from the enzymatic digestion of two standard proteins have been analyzed. Then, the algorithm's performance has been evaluated by comparing the results with other achieved using state-of-the-art alignment tools. In the second step, our algorithm has been used for the alignment of high-complexity LC-MS data consisting of peptides obtained by an Escherichia coli lysate available from a public repository previously used for the comparison of other alignment tools. MassUntangler gave excellent results in terms of precision scores (from 80% to 93%) and recall scores (from 68% to 89%), showing performances similar and even better than the previous developed tools. Considering the mass spectrometry sensitivity and accuracy, this approach allows the identification and quantification of peptides present in a biological sample at femtomole level with high confidence. The procedure's capability of aligning LC-MS data previously corrected for distortion in retention time has been studied through a hybrid approach, in which MassUntangler was interfaced with the OpenMS TOPP tool MapAligner. The hybrid aligner yielded better results, showing that an integration of different bioinformatic approaches for accurate label-free LC-MS data alignment should be used.

OryzaPG-DB: rice proteome database based on shotgun proteogenomics

BACKGROUND

Proteogenomics aims to utilize experimental proteome information for refinement of genome annotation. Since mass spectrometry-based shotgun proteomics approaches provide large-scale peptide sequencing data with high throughput, a data repository for shotgun proteogenomics would represent a valuable source of gene expression evidence at the translational level for genome re-annotation.

DESCRIPTION

Here, we present OryzaPG-DB, a rice proteome database based on shotgun proteogenomics, which incorporates the genomic features of experimental shotgun proteomics data. This version of the database was created from the results of 27 nanoLC-MS/MS runs on a hybrid ion trap-orbitrap mass spectrometer, which offers high accuracy for analyzing tryptic digests from undifferentiated cultured rice cells. Peptides were identified by searching the product ion spectra against the protein, cDNA, transcript and genome databases from Michigan State University, and were mapped to the rice genome. Approximately 3200 genes were covered by these peptides and 40 of them contained novel genomic features. Users can search, download or navigate the database per chromosome, gene, protein, cDNA or transcript and download the updated annotations in standard GFF3 format, with visualization in PNG format. In addition, the database scheme of OryzaPG was designed to be generic and can be reused to host similar proteogenomic information for other species. OryzaPG is the first proteogenomics-based database of the rice proteome, providing peptide-based expression profiles, together with the corresponding genomic origin, including the annotation of novelty for each peptide.

CONCLUSIONS

The OryzaPG database was constructed and is freely available at http://oryzapg.iab.keio.ac.jp/.

Comparative proteogenomic analysis of the Leptospira interrogans virulence-attenuated strain IPAV against the pathogenic strain 56601

The virulence-attenuated Leptospira interrogans serovar Lai strain IPAV was derived by prolonged laboratory passage from a highly virulent ancestral strain isolated in China. We studied the genetic variations of IPAV that render it avirulent via comparative analysis against the pathogenic L. interrogans serovar Lai strain 56601. The complete genome sequence of the IPAV strain was determined and used to compare with, and then rectify and reannotate the genome sequence of strain 56601. Aside from their highly similar genomic structure and gene order, a total of 33 insertions, 53 deletions and 301 single-nucleotide variations (SNVs) were detected throughout the genome of IPAV directly affecting 101 genes, either in their 5' upstream region or within their coding region. Among them, the majority of the 44 functional genes are involved in signal transduction, stress response, transmembrane transport and nitrogen metabolism. Comparative proteomic analysis based on quantitative liquid chromatography (LC)-MS/MS data revealed that among 1 627 selected pairs of orthologs, 174 genes in the IPAV strain were upregulated, with enrichment mainly in classes of energy production and lipid metabolism. In contrast, 228 genes in strain 56601 were upregulated, with the majority enriched in the categories of protein translation and DNA replication/repair. The combination of genomic and proteomic approaches illustrated that altered expression or mutations in critical genes, such as those encoding a Ser/Thr kinase, carbon-starvation protein CstA, glutamine synthetase, GTP-binding protein BipA, ribonucleotide-diphosphate reductase and phosphate transporter, and alterations in the translational profile of lipoproteins or outer membrane proteins are likely to account for the virulence attenuation in strain IPAV.

The novel responses of ethambutol against Mycobacterium smegmatis mc²155 Revealed by proteomics analysis

Ethambutol (EMB), one of the effective anti-mycobacterial drugs, inhibits the biosynthesis of mycobacterium cell wall. To elucidate the molecular mechanism of EMB against tuberculosis (TB), Mycobacterium smegmatis mc²155 was employed as a model of mycobacterial system in this study. We compared the protein profiles on M. smegmatis mc²155 treated by EMB and untreated using fluorescence difference two-dimensional gel electrophoresis (2-D DIGE). A total of 40 differential protein spots were selected and 22 proteins were identified by HPLC-nano ESI-MS/MS analysis, including 16 over-expressed proteins and 6 under-expressed proteins. These proteins mainly affected energy metabolism, as well as synthesis and modification of macromolecules. The expressions of correspondent genes were confirmed by RT-PCR. This investigation provided some clues for searching potential drug targets.

Proteogenomics to discover the full coding content of genomes: a computational perspective

Proteogenomics has emerged as a field at the junction of genomics and proteomics. It is a loose collection of technologies that allow the search of tandem mass spectra against genomic databases to identify and characterize protein-coding genes. Proteogenomic peptides provide invaluable information for gene annotation, which is difficult or impossible to ascertain using standard annotation methods. Examples include confirmation of translation, reading-frame determination, identification of gene and exon boundaries, evidence for post-translational processing, identification of splice-forms including alternative splicing, and also, prediction of completely novel genes. For proteogenomics to deliver on its promise, however, it must overcome a number of technological hurdles, including speed and accuracy of peptide identification, construction and search of specialized databases, correction of sampling bias, and others. This article reviews the state of the art of the field, focusing on the current successes, and the role of computation in overcoming these challenges. We describe how technological and algorithmic advances have already enabled large-scale proteogenomic studies in many model organisms, including arabidopsis, yeast, fly, and human. We also provide a preview of the field going forward, describing early efforts in tackling the problems of complex gene structures, searching against genomes of related species, and immunoglobulin gene reconstruction.

Proteomics-based confirmation of protein expression and correction of annotation errors in the Brucella abortus genome

Background

Brucellosis is a major bacterial zoonosis affecting domestic livestock and wild mammals, as well as humans around the globe. While conducting proteomics studies to better understand Brucella abortus virulence, we consolidated the proteomic data collected and compared it to publically available genomic data.

Results

The proteomic data was compiled from several independent comparative studies of Brucella abortus that used either outer membrane blebs, cytosols, or whole bacteria grown in media, as well as intracellular bacteria recovered at different times following macrophage infection. We identified a total of 621 bacterial proteins that were differentially expressed in a condition-specific manner. For 305 of these proteins we provide the first experimental evidence of their expression. Using a custom-built protein sequence database, we uncovered 7 annotation errors. We provide experimental evidence of expression of 5 genes that were originally annotated as non-expressed pseudogenes, as well as start site annotation errors for 2 other genes.

Conclusions

An essential element for ensuring correct functional studies is the correspondence between reported genome sequences and subsequent proteomics studies. In this study, we have used proteomics evidence to confirm expression of multiple proteins previously considered to be putative, as well as correct annotation errors in the genome of Brucella abortus strain 2308.

Relative codon adaptation: a generic codon bias index for prediction of gene expression

The development of codon bias indices (CBIs) remains an active field of research due to their myriad applications in computational biology. Recently, the relative codon usage bias (RCBS) was introduced as a novel CBI able to estimate codon bias without using a reference set. The results of this new index when applied to Escherichia coli and Saccharomyces cerevisiae led the authors of the original publications to conclude that natural selection favours higher expression and enhanced codon usage optimization in short genes. Here, we show that this conclusion was flawed and based on the systematic oversight of an intrinsic bias for short sequences in the RCBS index and of biases in the small data sets used for validation in E. coli. Furthermore, we reveal that how the RCBS can be corrected to produce useful results and how its underlying principle, which we here term relative codon adaptation (RCA), can be made into a powerful reference-set-based index that directly takes into account the genomic base composition. Finally, we show that RCA outperforms the codon adaptation index (CAI) as a predictor of gene expression when operating on the CAI reference set and that this improvement is significantly larger when analysing genomes with high mutational bias.

Cell wall proteome analysis of Mycobacterium smegmatis strain MC2 155

Background

The usually non-pathogenic soil bacterium Mycobacterium smegmatis is commonly used as a model mycobacterial organism because it is fast growing and shares many features with pathogenic mycobacteria. Proteomic studies of M. smegmatis can shed light on mechanisms of mycobacterial growth, complex lipid metabolism, interactions with the bacterial environment and provide a tractable system for antimycobacterial drug development. The cell wall proteins are particularly interesting in this respect. The aim of this study was to construct a reference protein map for these proteins in M. smegmatis.

Results

A proteomic analysis approach, based on one dimensional polyacrylamide gel electrophoresis and LC-MS/MS, was used to identify and characterize the cell wall associated proteins of M. smegmatis. An enzymatic cell surface shaving method was used to determine the surface-exposed proteins. As a result, a total of 390 cell wall proteins and 63 surface-exposed proteins were identified. Further analysis of the 390 cell wall proteins provided the theoretical molecular mass and pI distributions and determined that 26 proteins are shared with the surface-exposed proteome. Detailed information about functional classification, signal peptides and number of transmembrane domains are given next to discussing the identified transcriptional regulators, transport proteins and the proteins involved in lipid metabolism and cell division.

Conclusion

In short, a comprehensive profile of the M. smegmatis cell wall subproteome is reported. The current research may help the identification of some valuable vaccine and drug target candidates and provide foundation for the future design of preventive, diagnostic, and therapeutic strategies against mycobacterial diseases.

Acid stress response of a mycobacterial proteome: insight from a gene ontology analysis

Acidity in vesicles of macrophages is a general signal that bacteria respond to during infection. Mycobacteria are particularly capable of resisting the acidification in macrophages that engulf the bacteria. In this work, we used label-free quantitative proteomics to study the Mycobacterium smegmatis proteome under acid stress so as to gain an insight into the acidic adaptation in mycobacteria. We quantified 1032 proteins. With a 3-fold change threshold, 20 and 52 proteins were found regulated at false discovery rates of 5% and 14% respectively. We performed a systems analysis based on gene ontology for the global proteome expression profile. We found that the most significant changes induced by the acid stress include a downregulation of transmembrane transporter activity and an upregulation of enzymes involved in fatty acid metabolism. The results suggest that reduced transmembrane transport and increased fatty acid metabolism probably contribute to or associate with acid tolerance in mycobacteria.

Proteomics-based refinement of Deinococcus deserti genome annotation reveals an unwonted use of non-canonical translation initiation codons

Deinococcaceae are a family of extremely radiation-tolerant bacteria that are currently subjected to numerous studies aimed at understanding the molecular mechanisms for such radiotolerance. To achieve a comprehensive and accurate annotation of the Deinococcus deserti genome, we performed an N terminus-oriented characterization of its proteome. For this, we used a labeling reagent, N-tris(2,4,6-trimethoxyphenyl)phosphonium acetyl succinimide, to selectively derivatize protein N termini. The large scale identification of N-tris(2,4,6-trimethoxyphenyl)phosphonium acetyl succinimide-modified N-terminal-most peptides by shotgun liquid chromatography-tandem mass spectrometry analysis led to the validation of 278 and the correction of 73 translation initiation codons in the D. deserti genome. In addition, four new genes were detected, three located on the main chromosome and one on plasmid P3. We also analyzed signal peptide cleavages on a genome-wide scale. Based on comparative proteogenomics analysis, we propose a set of 137 corrections to improve Deinococcus radiodurans and Deinococcus geothermalis gene annotations. Some of these corrections affect important genes involved in DNA repair mechanisms such as polA, ligA, and ddrB. Surprisingly, experimental evidences were obtained indicating that DnaA (the protein involved in the DNA replication initiation process) and RpsL (the S12 ribosomal conserved protein) translation is initiated in Deinococcaceae from non-canonical codons (ATC and CTG, respectively). Such use may be the basis of specific regulation mechanisms affecting replication and translation. We also report the use of non-conventional translation initiation codons for two other genes: Deide_03051 and infC. Whether such use of non-canonical translation initiation codons is much more frequent than for other previously reported bacterial phyla or restricted to Deinococcaceae remains to be investigated. Our results demonstrate that predicting translation initiation codons is still difficult for some bacteria and that proteomics-based refinement of genome annotations may be helpful in such cases.

Toxoplasma gondii proteomics

Toxoplasma gondii is a ubiquitous, Apicomplexan parasite that, in humans, can cause several clinical syndromes, including encephalitis, chorioretinitis and congenital infection. T. gondii was described a little over 100 years ago in the tissues of the gundi (Ctenodoactylus gundi). There are a large number of applicable experimental techniques available for this pathogen and it has become a model organism for the study of intracellular pathogens. With the completion of the genomes for a type I (GT-1), type II (ME49) and type III (VEG) strains, proteomic studies on this organism have been greatly facilitated. Several subcellular proteomic studies have been completed on this pathogen. These studies have helped elucidate specialized invasion organelles and their composition, as well as proteins associated with the cytoskeleton. Global proteomic studies are leading to improved strategies for genome annotation in this organism and an improved understanding of protein regulation in this pathogen. Web-based resources, such as EPIC-DB and ToxoDB, provide proteomic data and support for studies on T. gondii. This review will summarize the current status of proteomic research on T. gondii.

A perfect genome annotation is within reach with the proteomics and genomics alliance

High-throughput identification of proteins and their accurate partial sequencing by shotgun nanoLC-MS/MS are now feasible for any cellular model at a full genomic scale. Proteogenomics is the integration of these data with the genome. Mining microbial proteomes allows validation of predicted orphan genes and correction of genome annotation errors such as discovery of unannotated genes, reversal of reading frames and identification of translational start sites, stop codon read-throughs or programmed frameshifts. Recent advances have been achieved in database searches, N-terminal oriented proteomics and homology-driven proteogenomics. From now on, proteogenomics on newly sequenced model genomes can be carried out at the earliest stage of the genome project as already exemplified by Mycoplasma mobile and Deinococcus deserti genomes. The proteomics and genomics alliance produces almost complete and accurate gene catalogues for small microbial genomes, a comprehensiveness which is essential for efficient systems biology.

EPIC-DB: a proteomics database for studying Apicomplexan organisms

Background

High throughput proteomics experiments are useful for analyzing the protein expression of an organism, identifying the correct gene structure of a genome, or locating possible post-translational modifications within proteins. High throughput methods necessitate publicly accessible and easily queried databases for efficiently and logically storing, displaying, and analyzing the large volume of data.

Description

EPICDB is a publicly accessible, queryable, relational database that organizes and displays experimental, high throughput proteomics data for Toxoplasma gondii and Cryptosporidium parvum. Along with detailed information on mass spectrometry experiments, the database also provides antibody experimental results and analysis of functional annotations, comparative genomics, and aligned expressed sequence tag (EST) and genomic open reading frame (ORF) sequences. The database contains all available alternative gene datasets for each organism, which comprises a complete theoretical proteome for the respective organism, and all data is referenced to these sequences. The database is structured around clusters of protein sequences, which allows for the evaluation of redundancy, protein prediction discrepancies, and possible splice variants. The database can be expanded to include genomes of other organisms for which proteome-wide experimental data are available.

Conclusion

EPICDB is a comprehensive database of genome-wide T. gondii and C. parvum proteomics data and incorporates many features that allow for the analysis of the entire proteomes and/or annotation of specific protein sequences. EPICDB is complementary to other -genomics- databases of these organisms by offering complete mass spectrometry analysis on a comprehensive set of all available protein sequences.

Identification of novel alternative splice isoforms of circulating proteins in a mouse model of human pancreatic cancer

To assess the potential of tumor-associated, alternatively spliced gene products as a source of biomarkers in biological fluids, we have analyzed a large data set of mass spectra derived from the plasma proteome of a mouse model of human pancreatic ductal adenocarcinoma. MS/MS spectra were interrogated for novel splice isoforms using a nonredundant database containing an exhaustive three-frame translation of Ensembl transcripts and gene models from ECgene. This integrated analysis identified 420 distinct splice isoforms, of which 92 did not match any previously annotated mouse protein sequence. We chose seven of those novel variants for validation by reverse transcription-PCR. The results were concordant with the proteomic analysis. All seven novel peptides were successfully amplified in pancreas specimens from both wild-type and mutant mice. Isotopic labeling of cysteine-containing peptides from tumor-bearing mice and wild-type controls enabled relative quantification of the proteins. Differential expression between tumor-bearing and control mice was notable for peptides from novel variants of muscle pyruvate kinase, malate dehydrogenase 1, glyceraldehyde-3-phosphate dehydrogenase, proteoglycan 4, minichromosome maintenance, complex component 9, high mobility group box 2, and hepatocyte growth factor activator. Our results show that, in a mouse model for human pancreatic cancer, novel and differentially expressed alternative splice isoforms are detectable in plasma and may be a source of candidate biomarkers.

Computational analysis and experimental validation of gene predictions in Toxoplasma gondii

BACKGROUND

Toxoplasma gondii is an obligate intracellular protozoan that infects 20 to 90% of the population. It can cause both acute and chronic infections, many of which are asymptomatic, and, in immunocompromised hosts, can cause fatal infection due to reactivation from an asymptomatic chronic infection. An essential step towards understanding molecular mechanisms controlling transitions between the various life stages and identifying candidate drug targets is to accurately characterize the T. gondii proteome.

METHODOLOGY/PRINCIPAL FINDINGS

We have explored the proteome of T. gondii tachyzoites with high throughput proteomics experiments and by comparison to publicly available cDNA sequence data. Mass spectrometry analysis validated 2,477 gene coding regions with 6,438 possible alternative gene predictions; approximately one third of the T. gondii proteome. The proteomics survey identified 609 proteins that are unique to Toxoplasma as compared to any known species including other Apicomplexan. Computational analysis identified 787 cases of possible gene duplication events and located at least 6,089 gene coding regions. Commonly used gene prediction algorithms produce very disparate sets of protein sequences, with pairwise overlaps ranging from 1.4% to 12%. Through this experimental and computational exercise we benchmarked gene prediction methods and observed false negative rates of 31 to 43%.

CONCLUSIONS/SIGNIFICANCE

This study not only provides the largest proteomics exploration of the T. gondii proteome, but illustrates how high throughput proteomics experiments can elucidate correct gene structures in genomes.

Ortho-proteogenomics: multiple proteomes investigation through orthology and a new MS-based protocol

The progress in sequencing technologies irrigates biology with an ever-increasing number of genome sequences. In most cases, the gene repertoire is predicted in silico and conceptually translated into proteins. As recently highlighted, the predicted genes exhibit frequent errors, particularly in start codons, with a serious impact on subsequent biological studies. A new "ortho-proteogenomic" approach is presented here for the annotation refinement of multiple genomes at once. It combines comparative genomics with an original proteomic protocol that allows the characterization of both N-terminal and internal peptides in a single experiment. This strategy was applied to the Mycobacterium genus with Mycobacterium smegmatis as the reference, and identified 946 distinct proteins, including 443 characterized N termini. These experimental data allowed the correction of 19% of the characterized start codons, the identification of 29 proteins missed during the annotation process, and the curation, thanks to comparative genomics, of 4328 sequences of 16 other Mycobacterium proteomes.

Regulation of glutamate metabolism by protein kinases in mycobacteria

Protein kinase G of Mycobacterium tuberculosis has been implicated in virulence and in regulation of glutamate metabolism. Here we show that this kinase undergoes a pattern of autophosphorylation that is distinct from that of other M. tuberculosis protein kinases characterized to date and we identify GarA as a substrate for phosphorylation by PknG. Autophosphorylation of PknG has little effect on kinase activity but promotes binding to GarA, an interaction that is also detected in living mycobacteria. PknG phosphorylates GarA at threonine 21, adjacent to the residue phosphorylated by PknB (T22), and these two phosphorylation events are mutually exclusive. Like the homologue OdhI from Corynebacterium glutamicum, the unphosphorylated form of GarA is shown to inhibit alpha-ketoglutarate decarboxylase in the TCA cycle. Additionally GarA is found to bind and modulate the activity of a large NAD(+)-specific glutamate dehydrogenase with an unusually low affinity for glutamate. Previous reports of a defect in glutamate metabolism caused by pknG deletion may thus be explained by the effect of unphosphorylated GarA on these two enzyme activities, which may also contribute to the attenuation of virulence.

Assessment of data processing to improve reliability of microarray experiments using genomic DNA reference

Background

Using genomic DNA as common reference in microarray experiments has recently been tested by different laboratories. Conflicting results have been reported with regard to the reliability of microarray results using this method. To explain it, we hypothesize that data processing is a critical element that impacts the data quality.

Results

Microarray experiments were performed in a γ-proteobacterium Shewanella oneidensis. Pair-wise comparison of three experimental conditions was obtained either with two labeled cDNA samples co-hybridized to the same array, or by employing Shewanella genomic DNA as a standard reference. Various data processing techniques were exploited to reduce the amount of inconsistency between both methods and the results were assessed. We discovered that data quality was significantly improved by imposing the constraint of minimal number of replicates, logarithmic transformation and random error analyses.

Conclusion

These findings demonstrate that data processing significantly influences data quality, which provides an explanation for the conflicting evaluation in the literature. This work could serve as a guideline for microarray data analysis using genomic DNA as a standard reference.

Critical assessment of alignment procedures for LC-MS proteomics and metabolomics measurements

Background

Liquid chromatography coupled to mass spectrometry (LC-MS) has become a prominent tool for the analysis of complex proteomics and metabolomics samples. In many applications multiple LC-MS measurements need to be compared, e. g. to improve reliability or to combine results from different samples in a statistical comparative analysis. As in all physical experiments, LC-MS data are affected by uncertainties, and variability of retention time is encountered in all data sets. It is therefore necessary to estimate and correct the underlying distortions of the retention time axis to search for corresponding compounds in different samples. To this end, a variety of so-called LC-MS map alignment algorithms have been developed during the last four years. Most of these approaches are well documented, but they are usually evaluated on very specific samples only. So far, no publication has been assessing different alignment algorithms using a standard LC-MS sample along with commonly used quality criteria.

Results

We propose two LC-MS proteomics as well as two LC-MS metabolomics data sets that represent typical alignment scenarios. Furthermore, we introduce a new quality measure for the evaluation of LC-MS alignment algorithms. Using the four data sets to compare six freely available alignment algorithms proposed for the alignment of metabolomics and proteomics LC-MS measurements, we found significant differences with respect to alignment quality, running time, and usability in general.

Conclusion

The multitude of available alignment methods necessitates the generation of standard data sets and quality measures that allow users as well as developers to benchmark and compare their map alignment tools on a fair basis. Our study represents a first step in this direction. Currently, the installation and evaluation of the "correct" parameter settings can be quite a time-consuming task, and the success of a particular method is still highly dependent on the experience of the user. Therefore, we propose to continue and extend this type of study to a community-wide competition. All data as well as our evaluation scripts are available at .

Spectral dictionaries: Integrating de novo peptide sequencing with database search of tandem mass spectra

Database search tools identify peptides by matching tandem mass spectra against a protein database. We study an alternative approach when all plausible de novo interpretations of a spectrum (spectral dictionary) are generated and then quickly matched against the database. We present a new MS-Dictionary algorithm for efficiently generating spectral dictionaries and demonstrate that MS-Dictionary can identify spectra that are missed in the database search. We argue that MS-Dictionary enables proteogenomics searches in six-frame translation of genomic sequences that may be prohibitively time-consuming for existing database search approaches. We show that such searches allow one to correct sequencing errors and find programmed frameshifts.

The proteome of Toxoplasma gondii: integration with the genome provides novel insights into gene expression and annotation

A proteomics analysis identifies one third of the predicted Toxoplasma gondii proteins and integrates proteomics and genomics data to refine genome annotation.

Background

Although the genomes of many of the most important human and animal pathogens have now been sequenced, our understanding of the actual proteins expressed by these genomes and how well they predict protein sequence and expression is still deficient. We have used three complementary approaches (two-dimensional electrophoresis, gel-liquid chromatography linked tandem mass spectrometry and MudPIT) to analyze the proteome of Toxoplasma gondii, a parasite of medical and veterinary significance, and have developed a public repository for these data within ToxoDB, making for the first time proteomics data an integral part of this key genome resource.

Results

The draft genome for Toxoplasma predicts around 8,000 genes with varying degrees of confidence. Our data demonstrate how proteomics can inform these predictions and help discover new genes. We have identified nearly one-third (2,252) of all the predicted proteins, with 2,477 intron-spanning peptides providing supporting evidence for correct splice site annotation. Functional predictions for each protein and key pathways were determined from the proteome. Importantly, we show evidence for many proteins that match alternative gene models, or previously unpredicted genes. For example, approximately 15% of peptides matched more convincingly to alternative gene models. We also compared our data with existing transcriptional data in which we highlight apparent discrepancies between gene transcription and protein expression.

Conclusion

Our data demonstrate the importance of protein data in expression profiling experiments and highlight the necessity of integrating proteomic with genomic data so that iterative refinements of both annotation and expression models are possible.

A comparative proteomic analysis of Gluconacetobacter diazotrophicus PAL5 at exponential and stationary phases of cultures in the presence of high and low levels of inorganic nitrogen compound

A proteomic view of G. diazotrophicus PAL5 at the exponential (E) and stationary phases (S) of cultures in the presence of low (L) and high levels (H) of combined nitrogen is presented. The proteomes analyzed on 2D-gels showed 131 proteins (42E+32S+29H+28L) differentially expressed by G. diazotrophicus, from which 46 were identified by combining mass spectrometry and bioinformatics tools. Proteins related to cofactor, energy and DNA metabolisms and cytoplasmic pH homeostasis were differentially expressed in E growth phase, under L and H conditions, in line with the high metabolic rate of the cells and the low pH of the media. Proteins most abundant in S-phase cells were stress associated and transporters plus transferases in agreement with the general phenomenon that binding protein-dependent systems are induced under nutrient limitation as part of hunger response. Cells grown in L condition produced nitrogen-fixation accessory proteins with roles in biosynthesis and stabilization of the nitrogenase complex plus proteins for protection of the nitrogenases from O(2)-induced inactivation. Proteins of the cell wall biogenesis apparatus were also expressed under nitrogen limitation and might function in the reshaping of the nitrogen-fixing G. diazotrophicus cells previously described. Genes whose protein products were detected in our analysis were mapped onto the chromosome and, based on the tendency of functionally related bacterial genes to cluster, we identified genes of particular pathways that could be organized in operons and are co-regulated. These results showed the great potential of proteomics to describe events in G. diazotrophicus cells by looking at proteins expressed under distinct growth conditions.

Comparative proteogenomics: combining mass spectrometry and comparative genomics to analyze multiple genomes

Recent proliferation of low-cost DNA sequencing techniques will soon lead to an explosive growth in the number of sequenced genomes and will turn manual annotations into a luxury. Mass spectrometry recently emerged as a valuable technique for proteogenomic annotations that improves on the state-of-the-art in predicting genes and other features. However, previous proteogenomic approaches were limited to a single genome and did not take advantage of analyzing mass spectrometry data from multiple genomes at once. We show that such a comparative proteogenomics approach (like comparative genomics) allows one to address the problems that remained beyond the reach of the traditional "single proteome" approach in mass spectrometry. In particular, we show how comparative proteogenomics addresses the notoriously difficult problem of "one-hit-wonders" in proteomics, improves on the existing gene prediction tools in genomics, and allows identification of rare post-translational modifications. We therefore argue that complementing DNA sequencing projects by comparative proteogenomics projects can be a viable approach to improve both genomic and proteomic annotations.

The proteomic response of Mycobacterium smegmatis to anti-tuberculosis drugs suggests targeted pathways

Mycobacterium smegmatis is a fast-growing model mycobacterial system that shares many features with the pathogenic Mycobacterium tuberculosis while allowing practical proteomics analysis. With the use of shotgun-style mass spectrometry, we provide a large-scale analysis of the M. smegmatis proteomic response to the anti-tuberculosis (TB) drugs isoniazid, ethambutol, and 5-chloropyrazinamide and elucidate the drugs' systematic effects on mycobacterial proteins. A total of 2550 proteins were identified with approximately 5% false-positive identification rate across 60 experiments, representing approximately 40% of the M. smegmatis proteome ( approximately 6500 proteins). Protein differential expression levels were estimated from the shotgun proteomics data, and 485 proteins showing altered expression levels in response to drugs were identified at a 99% confidence level. Proteomic comparison of anti-TB drug responses shows that translation, cell cycle control, and energy production are down-regulated in all three drug treatments. In contrast, systems related to the drugs' targets, such as lipid, amino acid, and nucleotide metabolism, show specific protein expression changes associated with a particular drug treatment. We identify proteins involved in target pathways for the three drugs and infer putative targets for 5-chloropyrazinamide.

Improving protein identification sensitivity by combining MS and MS/MS information for shotgun proteomics using LTQ-Orbitrap high mass accuracy data

We investigated and compared three approaches for shotgun protein identification by combining MS and MS/MS information using LTQ-Orbitrap high mass accuracy data. In the first approach, we employed a unique mass identifier method where MS peaks matched to peptides predicted from proteins identified from an MS/MS database search are first subtracted before using the MS peaks as unique mass identifiers for protein identification. In the second method, we used an accurate mass and time tag method by building a potential mass and retention time database from previous MudPIT analyses. For the third method, we used a peptide mass fingerprinting-like approach in combination with a randomized database for protein identification. We show that we can improve protein identification sensitivity for low-abundance proteins by combining MS and MS/MS information. Furthermore, "one-hit wonders" from MS/MS database searching can be further substantiated by MS information and the approach improves the identification of low-abundance proteins. The advantages and disadvantages for the three approaches are then discussed.

Whole proteome analysis of post-translational modifications: applications of mass-spectrometry for proteogenomic annotation

While bacterial genome annotations have significantly improved in recent years, techniques for bacterial proteome annotation (including post-translational chemical modifications, signal peptides, proteolytic events, etc.) are still in their infancy. At the same time, the number of sequenced bacterial genomes is rising sharply, far outpacing our ability to validate the predicted genes, let alone annotate bacterial proteomes. In this study, we use tandem mass spectrometry (MS/MS) to annotate the proteome of Shewanella oneidensis MR-1, an important microbe for bioremediation. In particular, we provide the first comprehensive map of post-translational modifications in a bacterial genome, including a large number of chemical modifications, signal peptide cleavages, and cleavages of N-terminal methionine residues. We also detect multiple genes that were missed or assigned incorrect start positions by gene prediction programs, and suggest corrections to improve the gene annotation. This study demonstrates that complementing every genome sequencing project by an MS/MS project would significantly improve both genome and proteome annotations for a reasonable cost.

A geometric approach for the alignment of liquid chromatography-mass spectrometry data

MOTIVATION

Liquid chromatography coupled to mass spectrometry (LC-MS) and combined with tandem mass spectrometry (LC-MS/MS) have become a prominent tool for the analysis of complex proteomic samples. An important step in a typical workflow is the combination of results from multiple LC-MS experiments to improve confidence in the obtained measurements or to compare results from different samples. To do so, a suitable mapping or alignment between the data sets needs to be estimated. The alignment has to correct for variations in mass and elution time which are present in all mass spectrometry experiments.

RESULTS

We propose a novel algorithm to align LC-MS samples and to match corresponding ion species across samples. Our algorithm matches landmark signals between two data sets using a geometric technique based on pose clustering. Variations in mass and retention time are corrected by an affine dewarping function estimated from matched landmarks. We use the pairwise dewarping in an algorithm for aligning multiple samples. We show that our pose clustering approach is fast and reliable as compared to previous approaches. It is robust in the presence of noise and able to accurately align samples with only few common ion species. In addition, we can easily handle different kinds of LC-MS data and adopt our algorithm to new mass spectrometry technologies.

AVAILABILITY

This algorithm is implemented as part of the OpenMS software library for shotgun proteomics and available under the Lesser GNU Public License (LGPL) at www.openms.de.

Diarylquinolines target subunit c of mycobacterial ATP synthase

The diarylquinoline R207910 (TMC207) is a promising candidate in clinical development for the treatment of tuberculosis. Though R207910-resistant mycobacteria bear mutations in ATP synthase, the compound's precise target is not known. Here we establish by genetic, biochemical and binding assays that the oligomeric subunit c (AtpE) of ATP synthase is the target of R207910. Thus targeting energy metabolism is a new, promising approach for antibacterial drug discovery.

A glucose kinase from Mycobacterium smegmatis

Carbon metabolism and regulation is poorly understood in mycobacteria, a genus that includes some major pathogenic species like Mycobacterium tuberculosis and Mycobacterium leprae. Here, we report the identification of a glucose kinase from Mycobacterium smegmatis. This enzyme serves in glucose metabolism and global carbon catabolite repression in the related actinomycete Streptomyces coelicolor. The gene, msmeg1356 (glkA), was found by means of in silico screening. It was shown that it occurs in the same genetic context in all so far sequenced mycobacterial species, where it is located in a putative tricistronic operon together with a glycosyl hydrolase and a putative malonyl-CoA transacylase. Heterologous expression of glkA in an Escherichia coli glucose kinase mutant led to the restoration of glucose growth, which provided in vivo evidence for glucose kinase function. GlkA(Msm) was subsequently overproduced in order to study its enzymatic features. We found that it can form a dimer and that it efficiently phosphorylates glucose at the expense of ATP. The affinity constant for glucose was with 9 mM about eight times higher and the velocity was about tenfold slower when compared to the parallel measured glucose kinase of S. coelicolor. Both enzymes showed similar substrate specificity, which consists in an ATP-dependent phosphorylation of glucose and no, or very inefficient, phosphorylation of the glucose analogues 2-deoxyglucose and methyl alpha-glucoside. Hence, our data provide a basis for studying the role of mycobacterial glucose kinase in vivo to unravel possible catalytic and regulatory functions.

Expression profiling in granulomatous lung disease

Granulomatous lung diseases, such as sarcoidosis, hypersensitivity pneumonitis, Wegener's granulomatosis, and chronic beryllium disease, along with granulomatous diseases of known infectious etiologies, such as tuberculosis, are major causes of morbidity and mortality throughout the world. Clinical manifestations of these diseases are highly heterogeneous, and the determinants of disease susceptibility and clinical course (e.g., resolution vs. chronic, progressive fibrosis) are largely unknown. The underlying pathogenic mechanisms of these diseases also remain poorly understood. Within this context, these diseases have been approached using genomic and proteomic technologies to allow us to identify patterns of gene/protein expression that track with clinical disease or to identify new pathways involved in disease pathogenesis. The results from these initial studies highlight the potential for these "-omics" approaches to reveal novel insights into the pathogenesis of granulomatous lung disease and provide new tools to improve diagnosis, clinical classification, course prediction, and response to therapy. Realizing this potential will require collaboration among multidisciplinary groups with expertise in the respective technologies, bioinformatics, and clinical medicine for these complex diseases.

Quantification of global transcription patterns in prokaryotes using spotted microarrays

We describe an analysis, applicable to any spotted microarray dataset produced using genomic DNA as a reference, that quantifies prokaryotic levels of mRNA on a genome-wide scale. Applying this to Mycobacterium tuberculosis, we validate the technique, show a correlation between level of expression and biological importance, define the complement of invariant genes and analyze absolute levels of expression by functional class to develop ways of understanding an organism's biology without comparison to another growth condition.

Interrupted coding sequences in Mycobacterium smegmatis: authentic mutations or sequencing errors?

BACKGROUND

In silico analysis has shown that all bacterial genomes contain a low percentage of ORFs with undetected frameshifts and in-frame stop codons. These interrupted coding sequences (ICDSs) may really be present in the organism or may result from misannotation based on sequencing errors. The reality or otherwise of these sequences has major implications for all subsequent functional characterization steps, including module prediction, comparative genomics and high-throughput proteomic projects.

RESULTS

We show here, using Mycobacterium smegmatis as a model species, that a significant proportion of these ICDSs result from sequencing errors. We used a resequencing procedure and mass spectrometry analysis to determine the nature of a number of ICDSs in this organism. We found that 28 of the 73 ICDSs investigated correspond to sequencing errors.

CONCLUSION

The correction of these errors results in modification of the predicted amino acid sequences of the corresponding proteins and changes in annotation. We suggest that each bacterial ICDS should be investigated individually, to determine its true status and to ensure that the genome sequence is appropriate for comparative genomics analyses.