Bioinformatic Prediction of Gene Ontology Terms of Uncharacterized Proteins from Chromosome 11.

In chromosome 11, 71 out of its 1254 proteins remain functionally uncharacterized on the basis of their existence evidence (uPE1s) following the latest version of neXtProt (release 2020-01-17). Because in vivo and in vitro experimental strategies are often time-consuming and labor-intensive, there is a need for a bioinformatics tool to predict the function annotation. Here, we used I-TASSER/COFACTOR provided on the neXtProt web site, which predicts gene ontology (GO) terms based on the 3D structure of the protein. I-TASSER/COFACTOR predicted 2413 GO terms with a benchmark dataset of the 22 proteins belonging to PE1 of chromosome 11. In this study, we developed a filtering algorithm in order to select specific GO terms using the GO map generated by I-TASSER/COFACTOR. As a result, 187 specific GO terms showed a higher average precision-recall score at the least cellular component term compared to 2413 predicted GO terms. Next, we applied 65 proteins belonging to uPE1s of chromosome 11, and then 409 out of 6684 GO terms survived, where 103 and 142 GO terms of molecular function and biological process, respectively, were included. Representatively, the cellular component GO terms of CCDC90B, C11orf52, and the SMAP were predicted and validated using the overexpression system into 293T cells and immunofluorescence staining. We will further study their biological and molecular functions toward the goal of the neXt-CP50 project as a part of C-HPP. We shared all results and programs in Github (https://github.com/heeyounh/I-TASSER-COFACTOR-filtering.git).

Next Generation Proteomic Pipeline for Chromosome-Based Proteomic Research Using NeXtProt and GENCODE Databases.

Human Proteome Project aims to map all human proteins including missing proteins as well as proteoforms with post translational modifications, alternative splicing variants (ASVs), and single amino acid variants (SAAVs). neXtProt and Ensemble databases are usually used to provide curated information on human coding genes. However, to find these proteoforms, we (Chr #11 team) first introduce a streamlined pipeline using customized and concatenated neXtProt and GENCODE originated from Ensemble, with controlled false discovery rate (FDR). Because of large sized databases used in this pipeline, we found more stringent FDR filtering (0.1% at the peptide level and 1% at the protein level) to claim novel findings, such as GENCODE ASVs and missing proteins, from human hippocampus data set (MSV000081385) and ProteomeXchange (PXD007166). Using our next generation proteomic pipeline (nextPP) with neXtProt and GENCODE databases, two missing proteins such as activity-regulated cytoskeleton-associated protein (ARC, Chr 8) and glutamate receptor ionotropic, kainite 5 (GRIK5, Chr 19) were additionally identified with two or more unique peptides from human brain tissues. Additionally, by applying the pipeline to human brain related data sets such as cortex (PXD000067 and PXD000561), spinal cord, and fetal brain (PXD000561), seven GENCODE ASVs such as ACTN4-012 (Chr.19), DPYSL2-005 (Chr.8), MPRIP-003 (Chr.17), NCAM1-013 (Chr.11), EPB41L1-017 (Chr.20), AGAP1-004 (Chr.2), and CPNE5-005 (Chr.6) were identified from two or more data sets. The identified peptides of GENCODE ASVs were mapped onto novel exon insertions, alternative translations at 5'-untranslated region, or novel protein coding sequence. Applying the pipeline to male reproductive organ related data sets, 52 GENCODE ASVs were identified from two testis (PXD000561 and PXD002179) and a spermatozoa (PXD003947) data sets. Four out of 52 GENCODE ASVs such as RAB11FIP5-008 (Chr. 2), RP13-347D8.7-001 (Chr. X), PRDX4-002 (Chr. X), and RP11-666A8.13-001 (Chr. 17) were identified in all of the three samples.

Integrated Proteomic Pipeline Using Multiple Search Engines for a Proteogenomic Study with a Controlled Protein False Discovery Rate.

In the Chromosome-Centric Human Proteome Project (C-HPP), false-positive identification by peptide spectrum matches (PSMs) after database searches is a major issue for proteogenomic studies using liquid-chromatography and mass-spectrometry-based large proteomic profiling. Here we developed a simple strategy for protein identification, with a controlled false discovery rate (FDR) at the protein level, using an integrated proteomic pipeline (IPP) that consists of four engrailed steps as follows. First, using three different search engines, SEQUEST, MASCOT, and MS-GF+, individual proteomic searches were performed against the neXtProt database. Second, the search results from the PSMs were combined using statistical evaluation tools including DTASelect and Percolator. Third, the peptide search scores were converted into E-scores normalized using an in-house program. Last, ProteinInferencer was used to filter the proteins containing two or more peptides with a controlled FDR of 1.0% at the protein level. Finally, we compared the performance of the IPP to a conventional proteomic pipeline (CPP) for protein identification using a controlled FDR of <1% at the protein level. Using the IPP, a total of 5756 proteins (vs 4453 using the CPP) including 477 alternative splicing variants (vs 182 using the CPP) were identified from human hippocampal tissue. In addition, a total of 10 missing proteins (vs 7 using the CPP) were identified with two or more unique peptides, and their tryptic peptides were validated using MS/MS spectral pattern from a repository database or their corresponding synthetic peptides. This study shows that the IPP effectively improved the identification of proteins, including alternative splicing variants and missing proteins, in human hippocampal tissues for the C-HPP. All RAW files used in this study were deposited in ProteomeXchange (PXD000395).

Combination of Multiple Spectral Libraries Improves the Current Search Methods Used to Identify Missing Proteins in the Chromosome-Centric Human Proteome Project.

Approximately 2.9 billion long base-pair human reference genome sequences are known to encode some 20 000 representative proteins. However, 3000 proteins, that is, ~15% of all proteins, have no or very weak proteomic evidence and are still missing. Missing proteins may be present in rare samples in very low abundance or be only temporarily expressed, causing problems in their detection and protein profiling. In particular, some technical limitations cause missing proteins to remain unassigned. For example, current mass spectrometry techniques have high limits and error rates for the detection of complex biological samples. An insufficient proteome coverage in a reference sequence database and spectral library also raises major issues. Thus, the development of a better strategy that results in greater sensitivity and accuracy in the search for missing proteins is necessary. To this end, we used a new strategy, which combines a reference spectral library search and a simulated spectral library search, to identify missing proteins. We built the human iRefSPL, which contains the original human reference spectral library and additional peptide sequence-spectrum match entries from other species. We also constructed the human simSPL, which contains the simulated spectra of 173 907 human tryptic peptides determined by MassAnalyzer (version 2.3.1). To prove the enhanced analytical performance of the combination of the human iRefSPL and simSPL methods for the identification of missing proteins, we attempted to reanalyze the placental tissue data set (PXD000754). The data from each experiment were analyzed using PeptideProphet, and the results were combined using iProphet. For the quality control, we applied the class-specific false-discovery rate filtering method. All of the results were filtered at a false-discovery rate of <1% at the peptide and protein levels. The quality-controlled results were then cross-checked with the neXtProt DB (2014-09-19 release). The two spectral libraries, iRefSPL and simSPL, were designed to ensure no overlap of the proteome coverage. They were shown to be complementary to spectral library searching and significantly increased the number of matches. From this trial, 12 new missing proteins were identified that passed the following criterion: at least 2 peptides of 7 or more amino acids in length or one of 9 or more amino acids in length with one or more unique sequences. Thus, the iRefSPL and simSPL combination can be used to help identify peptides that have not been detected by conventional sequence database searches with improved sensitivity and a low error rate.

Chromosome-Based Proteomic Study for Identifying Novel Protein Variants from Human Hippocampal Tissue Using Customized neXtProt and GENCODE Databases.

The goal of the Chromosome-Centric Human Proteome Project (C-HPP) is to fully provide proteomic information from each human chromosome, including novel proteoforms, such as novel protein-coding variants expressed from noncoding genomic regions, alternative splicing variants (ASVs), and single amino acid variants (SAAVs). In the 144 LC/MS/MS raw files from human hippocampal tissues of control, epilepsy, and Alzheimer's disease, we identified the novel proteoforms with a workflow including integrated proteomic pipeline using three different search engines, MASCOT, SEQUEST, and MS-GF+. With a <1% false discovery rate (FDR) at the protein level, the 11 detected peptides mapped to four translated long noncoding RNA variants against the customized databases of GENCODE lncRNA, which also mapped to coding-proteins at different chromosomal sites. We also identified four novel ASVs against the customized databases of GENCODE transcript. The target peptides from the variants were validated by tandem MS fragmentation pattern from their corresponding synthetic peptides. Additionally, a total of 128 SAAVs paired with their wild-type peptides were identified with FDR <1% at the peptide level using a customized database from neXtProt including nonsynonymous single nucleotide polymorphism (nsSNP) information. Among these results, several novel variants related in neuro-degenerative disease were identified using the workflow that could be applicable to C-HPP studies. All raw files used in this study were deposited in ProteomeXchange (PXD000395).

Large-scale analysis of posttranslational modifications in the hippocampus of patients with Alzheimer's disease using pI shift and label-free quantification without enrichment.

Posttranslational modifications modulate protein function in cells. Global analysis of multiple posttranslational modifications can provide insight into physiology and disease, but presents formidable challenges. In the present study, we used a technique that does not require target enrichment to analyze alterations in the phosphorylation and ubiquitination of proteins from patients with Alzheimer's disease (AD). Guided by our previous findings, we applied three strategies to further our understanding of the dysregulation of posttranslationally modified proteins. We first identified phosphorylation sites by determining peptide pI shifts using OFFGEL. Second, using tandem mass spectrometry, we determined the ubiquitination status of the proteins using an assay for a trypsin digestion remnant of ubiquitination (Gly-Gly). Third, for large-scale discovery, we quantified the global differences in protein expression. Of the proteins expressed in AD tissue at levels of 2.0 or greater compared with controls, 60 were phosphorylated and 56 were ubiquitinated. Of the proteins expressed at levels of 0.5 or lower compared with controls, 81 were phosphorylated and 56 were ubiquitinated. Approximately 98 % of the phosphopeptides exhibited a pI shift. We identified 112 new phosphorylation sites (51.38 %), and 92 new ubiquitination sites (96.84 %). Taken together, our findings suggest that analysis of the alterations in posttranslationally modified proteins may contribute to understanding the pathogenesis of AD and other diseases.

Chromosome 11-centric human proteome analysis of human brain hippocampus tissue.

Human chromosome 11 is the third gene-rich chromosome having 1304 protein-coding genes. According to the GeneCards, this chromosome contains 240 genes related to diseases, as it is well known as a disease-rich chromosome. Although there are many protein-coding genes, the proteomic identification ratio is rather low. As a model study, human hippocampal tissues from patients suffering from Alzheimer's disease and epilepsy were prepared to evaluate the gene-centric statistics related to the gene expression and disorders of chromosome 11. A total of 8828 protein coding genes from brain tissues were extensively off-gel fractionated and profiled by a high resolution mass spectrometer with collision induced dissociation and electron transfer dissociation. Five-hundred twenty-three of the proteins from brain tissues were determined to belong to chromosome 11, representing 37% of the proteins reported in the Global Proteome Machine Database. We extracted gene clusters from a specific biological process or molecular function in gene ontology, among which the olfactory receptor genes showed the largest cluster on chromosome 11. Analysis of the proteome data set from the hippocampus provides a significant network associated with genes and proteins and leads to new insights into the biological and genetic mechanisms of chromosome 11-specific diseases such as Alzheimer's disease.

Quantitative proteomics of auditory fear conditioning.

Auditory fear conditioning is a well-characterized rodent learning model where a neutral auditory cue is paired with an aversive outcome to induce associative fear memory. The storage of long-term auditory fear memory requires long-term potentiation (LTP) in the lateral amygdala and de novo protein synthesis. Although many studies focused on individual proteins have shown their contribution to LTP and fear conditioning, non-biased genome-wide studies have only recently been possible with microarrays, which nevertheless fall short of measuring changes at the level of proteins. Here we employed quantitative proteomics to examine the expression of hundreds of proteins in the lateral amygdala in response to auditory fear conditioning. We found that various proteins previously implicated in LTP, learning and axon/dendrite growth were regulated by fear conditioning. A substantial number of proteins that were regulated by fear conditioning have not yet been studied specifically in learning or synaptic plasticity.

Profiling and semiquantitative analysis of the cell surface proteome in human mesenchymal stem cells.

Mulitpotent mesenchymal stem cells (MSCs) derived from human bone marrow are promising candidates for the development of cell therapeutic strategies. MSC surface protein profiles provide novel biological knowledge concerning the proliferation and differentiation of these cells, including the potential for identifying therapeutic targets. Basic fibroblast growth factor (bFGF) affects cell surface proteins, which are associated with increased growth rate, differentiation potential, as well as morphological changes of MSCs in vitro. Cell surface proteins were isolated using a biotinylation-mediated method and identified using a combination of one-dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis and mass spectrometry. The resulting gel lines were cut into 20 bands and digested with trypsin. Each tryptic fragment was analyzed by liquid chromatography-electrospray ionization tandem mass spectrometry. Proteins were identified using the Mascot search program and the International Protein Index human database. Noble MSC surface proteins (n = 1,001) were identified from cells cultured either with (n = 857) or without (n = 667) bFGF-containing medium in three independent experiments. The proteins were classified using FatiGO to elucidate their function. We also confirmed the proteomics results using Western blotting and immunofluorescence microscopic analysis. The nature of the proteins identified makes it clear that MSCs express a wide variety of signaling molecules, including those related to cell differentiation. Among the latter proteins, four Ras-related Rab proteins, laminin-R, and three 14-3-3 proteins that were fractionated from MSCs cultured on bFGF-containing medium are implicated in bFGF-induced signal transduction of MSCs. Consequently, these finding provide insight into the understanding of the surface proteome of human MSCs.

Targeted mass spectrometric approach for biomarker discovery and validation with nonglycosylated tryptic peptides from N-linked glycoproteins in human plasma.

A simple mass spectrometric approach for the discovery and validation of biomarkers in human plasma was developed by targeting nonglycosylated tryptic peptides adjacent to glycosylation sites in an N-linked glycoprotein, one of the most important biomarkers for early detection, prognoses, and disease therapies. The discovery and validation of novel biomarkers requires complex sample pretreatment steps, such as depletion of highly abundant proteins, enrichment of desired proteins, or the development of new antibodies. The current study exploited the steric hindrance of glycan units in N-linked glycoproteins, which significantly affects the efficiency of proteolytic digestion if an enzymatically active amino acid is adjacent to the N-linked glycosylation site. Proteolytic digestion then results in quantitatively different peptide products in accordance with the degree of glycosylation. The effect of glycan steric hindrance on tryptic digestion was first demonstrated using alpha-1-acid glycoprotein (AGP) as a model compound versus deglycosylated alpha-1-acid glycoprotein. Second, nonglycosylated tryptic peptide biomarkers, which generally show much higher sensitivity in mass spectrometric analyses than their glycosylated counterparts, were quantified in human hepatocellular carcinoma plasma using a label-free method with no need for N-linked glycoprotein enrichment. Finally, the method was validated using a multiple reaction monitoring analysis, demonstrating that the newly discovered nonglycosylated tryptic peptide targets were present at different levels in normal and hepatocellular carcinoma plasmas. The area under the receiver operating characteristic curve generated through analyses of nonglycosylated tryptic peptide from vitronectin precursor protein was 0.978, the highest observed in a group of patients with hepatocellular carcinoma. This work provides a targeted means of discovering and validating nonglycosylated tryptic peptides as biomarkers in human plasma, without the need for complex enrichment processes or expensive antibody preparations.

Use of MDLC-DIGE and LC-MS/MS to identify serum biomarkers for complete remission in patients with acute myeloid leukemia.

The response criteria for complete remission (CR) in acute myeloid leukemia (AML) are currently based on morphology and blood cell counts. However, these criteria are insufficient to establish a diagnosis in cases with poor quality bone marrow (BM) samples demonstrating a loss of cellular morphology. We investigated whether the sera of patients contained biomarkers that indicate disease response status. First, we performed multidimensional liquid chromatography-differential gel electrophoresis (MDLC-DIGE) to generate protein profiles of two pooled, paired serum samples from patients who had achieved CR; one collected at diagnosis (PreCR) and the other collected after chemotherapy (CR). Then, with the biomarker candidates found, ELISA was carried out for individual PreCR and CR samples, and for other verification sets including nonremission (NR) patients and normal samples. We selected two proteins, complement factor H (CFH) and apolipoprotein H (ApoH), with dye (Cy) ratios showing greater than 2.0-fold differences between the pooled samples. ELISA showed that CFH and ApoH are useful for distinguishing between the recovered (CR and normal) and nonrecovered (PreCR, PreNR, and NR) states in AML (p <0.001). We successfully applied a protein profiling technology of MDLC-DIGE and LC-MS/MS to discover two biomarkers for CR which needs further validation for a clinical setting.

Quantitative proteomic analysis of cell wall and plasma membrane fractions from multidrug-resistant Acinetobacter baumannii.

Acinetobacter baumannii is a Gram-negative, nonmotile aerobic bacterium that has emerged as an important nosocomial pathogen. Multidrug-resistant (MDR) A. baumannii is difficult to treat with antibiotics, and treatment failure in infected patients is of great concern in clinical settings. To investigate proteome regulation in A. baumannii under antibiotic stress conditions, quantitative membrane proteomic analyses of a clinical MDR A. baumannii strain cultured in subminimal inhibitory concentrations of tetracycline and imipenem were performed using a combination of label-free (one-dimensional electrophoresis-liquid chromatography-tandem mass spectrometry) and label (isobaric tag for relative and absolute quantitation) approaches. In total, 484 proteins were identified, and 302 were classified as outer membrane, periplasmic, or plasma membrane proteins. The clinical A. baumannii strain DU202 responded specifically and induced different cell wall and membrane protein sets that provided resistance to the antibiotics. The induction of resistance-nodulation-cell division transporters and protein kinases, and the repression of outer membrane proteins were common responses in the presence of tetracycline and imipenem. Induction of a tetracycline resistant pump, ribosomal proteins, and iron-uptake transporters appeared to be dependent on tetracycline conditions, whereas ß-lactamase and penicillin-binding proteins appeared to be dependent on imipenem conditions. These results suggest that combined liquid chromatography-based proteomic approaches can be used to identify cell wall and membrane proteins involved in the antibiotic resistance of A. baumannii.

Tackling quantitation: a report on the annual Spring Workshop of the HUPO-PSI 28-30 March 2010, Seoul, South Korea.

The annual Spring Workshop of the HUPO-PSI took place in Korea, where the Mass Spectrometry and Protein Separations groups joined forces to tackle the issue of the consistent reporting of quantitative proteomic data generated by mass-spectrometry-based technologies. A preliminary mzQuantML schema was drafted which, when completed and tested, will complement the existing mzIdentML schema for reporting protein identifications. The Molecular Interactions group concentrated on the implementations of the PSICQUIC (PSI Common Query InterfaCe) service that allows users to simultaneously query interaction data across multiple participating resources. Work was also undertaken to update the MIAPE guidelines, in response to feedback from the editors of a number of proteomic journals.

Differential expression of cell surface proteins in human bone marrow mesenchymal stem cells cultured with or without basic fibroblast growth factor containing medium.

Mesenchymal stem cells (MSCs) are multipotent cells, which have the capability to differentiate into various mesenchymal tissues such as bone, cartilage, fat, tendon, muscle, and marrow stroma. However, they lose the capability of multi-lineage differentiation after several passages. It is known that basic fibroblast growth factor (bFGF) increases growth rate, differentiation potential, and morphological changes of MSCs in vitro. In this report, we have used 2-DE coupled to MS to identify differentially expressed proteins at the cell membrane level in MSCs growing in bFGF containing medium. The cell surface proteins isolated by the biotin-avidin affinity column were separated by 2-DE in triplicate experiments. A total of 15 differentially expressed proteins were identified by quadrupole-time of flight tandem MS. Nine of the proteins were upregulated and six proteins were downregulated in the MSCs cultured with bFGF containing medium. The expression level of three actin-related proteins, F-actin-capping protein subunit alpha-1, actin-related protein 2/3 complex subunit 2, and myosin regulatory light chain 2, was confirmed by Western blot analysis. The results indicate that the expression levels of F-actin-capping protein subunit alpha-1, actin-related protein 2/3 complex subunit 2, and myosin regulatory light chain 2 are important in bFGF-induced morphological change of MSCs.

Island clustering analysis for the comparison of the membrane and the soluble protein fractions of human brain proteome.

A protein identified in multiple separate bands of a 1-D gel reflects variation in the molecular weight caused by alternative splicing, endoproteolytic cleavage, or PTMs, such as glycosylation or ubiquitination. To characterize such a protein distribution over the bands, we defined an entity called an 'island' as the band region including the bands of the same protein identified sequentially. We quantified the island distribution using a new variable called an Iscore. Previously, as described in Park et al.. (Proteomics 2006, 6, 4978-4986.), we analyzed human brain tissue using a multidimensional MS/MS separation method. Here, the new method of island analysis was applied to the previous proteome data. The soluble and membrane protein fractions of human brain tissue were reanalyzed using the island distribution. The proteome of the soluble fraction exhibited more variation in island positions than that of the membrane fraction. Through the island analysis, we identified protein modifications and protein complexes over the 1-D gel bands.

Proteomic analysis of Korean ginseng (Panax ginseng C.A. Meyer).

Although many reports have been published regarding the pharmacological effects of ginseng, little is known about the biochemical pathways operant in ginsenoside biosynthesis, or the genes involved therein. Proteomics analysis is an approach to elucidate the physiological characteristics and biosynthetic pathways of ginsenosides, main components of ginseng. In this review, we introduced the recent progress in proteomics studies of ginseng (Panax ginseng C.A. Meyer). We briefly reference the genomic analyses of P. ginseng, without which proteomics approaches would have been impossible. Functional genomics studies regarding secondary metabolism in P. ginseng are also introduced here, in order to introduce possible future prospects for further study.

Metabolic changes in serum steroids induced by total-body irradiation of female C57B/6 mice.

The short- and long-term effects of a single exposure to gamma radiation on steroid metabolism were investigated in mice. Gas chromatography-mass spectrometry was used to generate quantitative profiles of serum steroid levels in mice that had undergone total-body irradiation (TBI) at doses of 0Gy, 1Gy, and 4Gy. Following TBI, serum samples were collected at the pre-dose time point and 1, 3, 6, and 9 months after TBI. Serum levels of progestins, progesterone, 5ß-DHP, 5α-DHP, and 20α-DHP showed a significant down-regulation following short-term exposure to 4Gy, with the exception of 20α-DHP, which was significantly decreased at each of the time points measured. The corticosteroids 5α-THDOC and 5α-DHB were significantly elevated at each of the time points measured after exposure to either 1 or 4Gy. Among the sterols, 24S-OH-cholestoerol showed a dose-related elevation after irradiation that reached significance in the high dose group at the 6- and 9-month time points.

The 3rd DBCLS BioHackathon: improving life science data integration with Semantic Web technologies.

BACKGROUND: BioHackathon 2010 was the third in a series of meetings hosted by the Database Center for Life Sciences (DBCLS) in Tokyo, Japan. The overall goal of the BioHackathon series is to improve the quality and accessibility of life science research data on the Web by bringing together representatives from public databases, analytical tool providers, and cyber-infrastructure researchers to jointly tackle important challenges in the area of in silico biological research. RESULTS: The theme of BioHackathon 2010 was the 'Semantic Web', and all attendees gathered with the shared goal of producing Semantic Web data from their respective resources, and/or consuming or interacting those data using their tools and interfaces. We discussed on topics including guidelines for designing semantic data and interoperability of resources. We consequently developed tools and clients for analysis and visualization. CONCLUSION: We provide a meeting report from BioHackathon 2010, in which we describe the discussions, decisions, and breakthroughs made as we moved towards compliance with Semantic Web technologies - from source provider, through middleware, to the end-consumer.

Inter-lab proteomics: data mining in collaborative projects on the basis of the HUPO brain proteome project's pilot studies.

Several projects were initiated by the Human Proteome Organisation (HUPO) focusing on the proteome analysis of distinct human organs. The initiative dedicated to the brain, its development and correlated diseases is the HUPO Brain Proteome Project (HUPO BPP). An objective data submission, storage, and reprocessing strategy have been established with the help of the results gained in a pilot study phase and within subsequent studies. The bioinformatic relevance of the data is drawn from the inter-laboratory comparisons as well as from the recalculation of all data sets submitted by the different groups. In the following, results of the single groups as well as the centralised reprocessing effort are summarised, demonstrating the added-value of this concerted work.

Proteomic characterization of the Pseudomonas putida KT2440 global response to a monocyclic aromatic compound by iTRAQ analysis and 1DE-MudPIT.

Pseudomonas putida KT2440 is a metabolically versatile soil bacterium. To examine the effects of an aromatic compound on the proteome of this bacterium, cytosolic proteins induced by the presence of benzoate and succinate were analyzed using two liquid chromatography (LC)-based proteomic approaches: an isobaric tag for relative and absolute quantitation (iTRAQ) for quantitative analysis and one-dimensional gel electrophoresis/multidimensional protein identification technology (1-DE MudPIT) for protein identification. In total, 1286 proteins were identified by 1-DE MudPIT; this represents around 23.3% of the total proteome. In contrast, 570 proteins were identified and quantified by iTRAQ analysis. Of these, 55 and 52 proteins were up- and down-regulated, respectively, in the presence of benzoate. The proteins up-regulated included benzoate degradation enzymes, chemotaxis-related proteins, and ABC transporters. Enzymes related to nitrogen metabolism and pyruvate metabolism were down-regulated. These data suggest that a combination of 1-DE MudPIT and iTRAQ is an appropriate method for comprehensive proteomic analysis of biodegradative bacteria.