Proteomic analysis of rutin-induced secreted proteins from Aspergillus flavus

Few studies have been conducted to identify the extracellular proteins and enzymes secreted by filamentous fungi, particularly with respect to dispensable metabolic pathways. Proteomic analysis has proven to be the most powerful method for identification of proteins in complex mixtures and is suitable for the study of the alteration of protein expression under different environmental conditions. The filamentous fungus Aspergillus flavus can degrade the flavonoid rutin as the only source of carbon via an extracellular enzyme system. In this study, a proteomic analysis was used to differentiate and identify the extracellular rutin-induced and non-induced proteins secreted by A. flavus. The secreted proteins were analyzed by two-dimensional electrophoresis and MALDI-TOF mass spectrometry. While 15 rutin-induced proteins and 7 non-induced proteins were identified, more than 90 protein spots remain unidentified, indicating that these proteins are either novel proteins or proteins that have not yet been sequenced.     

Local database and the search program for proteomic analysis of sperm proteins in the ascidian Ciona intestinalis

Separation of proteins by two-dimensional electrophoresis and following mass spectrometry (MS) is now a conventional technique for proteomic analysis. For proteomic analysis of a certain tissue with a limited information of primary structures of proteins, we have developed an analytical system for peptide mass fingerprinting in gene products in the testis of the ascidian Ciona intestinalis. Ciona sperm proteins were separated by two-dimensional gel electrophoresis and the tryptic fragments were subjected to MALDI-TOF/MS. The mass pattern was searched against on-line databases but resulted in less identification of these proteins. We have constructed a MS database from Ciona testis ESTs and the genome draft sequence, along with a newly devised, perl-based search program PerMS for peptide mass fingerprinting. This system could identify more than 80% of Ciona sperm proteins, suggesting that it could be widely applied for proteomic analysis for a limited tissue with less genomic information.     

Evaluating peptide mass fingerprinting-based protein identification

Identification of proteins by mass spectrometry (MS) is an essential step in proteomic studies and is typically accomplished by either peptide mass fingerprinting (PMF) or amino acid sequencing of the peptide. Although sequence information from MS/MS analysis can be used to validate PMF-based protein identification, it may not be practical when analyzing a large number of proteins and when high- throughput MS/MS instrumentation is not readily available. At present, a vast majority of proteomic studies employ PMF. However, there are huge disparities in criteria used to identify proteins using PMF. Therefore, to reduce incorrect protein identification using PMF, and also to increase confidence in PMF-based protein identification without accompanying MS/MS analysis, definitive guiding principles are essential. To this end, we propose a value-based scoring system that provides guidance on evaluating when PMF-based protein identification can be deemed sufficient without accompanying amino acid sequence data from MS/MS analysis.     

Evaluation of different multidimensional LC-MS/MS pipelines for isobaric tags for relative and absolute quantitation (iTRAQ)-based proteomic analysis of potato tubers in response to cold storage

Cold-induced sweetening in potato tubers is a costly problem for the food industry. To systematically identify the proteins associated with this process, we employed a comparative proteomics approach using isobaric, stable isotope coded labels to compare the proteomes of potato tubers after 0 and 5 months of storage at 5 °C. We evaluated both high pH reverse phase (hpRP) liquid chromatography (LC) and off-gel electrophoresis (OGE) as first dimension fractionation methods followed by nanoLC-MS/MS, using two high performance mass spectrometry platforms (Q-TOF and Orbitrap). We found that hpRP-LC consistently offered better resolution, reduced expression ratio compression, and a more MS-compatible workflow than OGE and consistently yielded more unique peptide/protein identifications and higher sequence coverage with better quantification. In this study, a total of 4463 potato proteins were identified, of which 46 showed differential expressions during potato tuber cold storage. Several key proteins important in controlling starch-sugar conversion, which leads to cold-induced sweetening, as well as other proteins that are potentially involved in this process, were identified. Our results suggest that the hpRP-RP shotgun approach is a feasible and practical workflow for discovering potential protein candidates in plant proteomic analysis.     

1-DE MS and 2-D LC-MS analysis of the mouse bronchoalveolar lavage proteome

Bronchoalveolar lavage fluid (BALF) is a complex mixture of proteins, which represents a unique clinically useful sampling of the lower respiratory tract. Many proteomic technologies can be used to characterize complex biological mixtures; however, it is not yet clear which technology(s) provide more information regarding the number of proteins identified and sequence coverage. In this study, we initially compared two common proteomic approaches, 2-D LC microESI MS/MS and 1-DE followed by gel slice digestion, peptide extraction and peptide identification by MS in characterization of the mouse BALF proteome; secondly, we identified 297 unique proteins from the mouse BALF proteome, greatly expanded the BALF proteome by about threefold regardless of species.     

Proteomics analysis of “Rovabio™ Excel”, a secreted protein cocktail from the filamentous fungus <Emphasis Type="Italic">Penicillium funiculosum</Emphasis> grown under industrial process fermentation

MS/MS techniques are well customized now for proteomic analysis, even for non-sequenced organisms, since peptide sequences obtained by these methods can be matched with those found in databases from closely related sequenced organisms. We used this approach to characterize the protein content of the “Rovabio™ Excel”, an enzymatic cocktail produced by Penicillium funiculosum that is used as feed additive in animal nutrition. Protein separation by bi-dimensional electrophoresis yielded more than 100 spots, from which 37 proteins were unambiguously assigned from peptide sequences. By one-dimensional SDS-gel electrophoresis, 34 proteins were identified among which 8 were not found in the 2-DE analysis. A third method, termed ‘peptidic shotgun’, which consists in a direct treatment of the cocktail by trypsin followed by separation of the peptides on two-dimensional liquid chromatography, resulted in the identification of two additional proteins not found by the two other methods. Altogether, more than 50 proteins, among which several glycosylhydrolytic, hemicellulolytic and proteolytic enzymes, were identified by combining three separation methods in this enzymatic cocktail. This work confirmed the power of proteome analysis to explore the genome expression of a non-sequenced fungus by taking advantage of sequences from phylogenetically related filamentous fungi and pave the way for further functional analysis of P. funiculosum.     

Evaluating Peptide Mass Fingerprinting-based Protein Identification

Identification of proteins by mass spectrometry (MS) is an essential step in pro- teomic studies and is typically accomplished by either peptide mass fingerprinting (PMF) or amino acid sequencing of the peptide. Although sequence information from MS/MS analysis can be used to validate PMF-based protein identification, it may not be practical when analyzing a large number of proteins and when high- throughput MS/MS instrumentation is not readily available. At present, a vast majority of proteomic studies employ PMF. However, there are huge disparities in criteria used to identify proteins using PMF. Therefore, to reduce incorrect protein identification using PMF, and also to increase confidence in PMF-based protein identification without accompanying MS/MS analysis, definitive guiding principles are essential. To this end, we propose a value-based scoring system that provides guidance on evaluating when PMF-based protein identification can be deemed sufficient without accompanying amino acid sequence data from MS/MS analysis.     

Evaluating Peptide Mass Fingerprinting-based Protein Identification

Identification of proteins by mass spectrometry (MS) is an essential step in pro- teomic studies and is typically accomplished by either peptide mass fingerprinting (PMF) or amino acid sequencing of the peptide. Although sequence information from MS/MS analysis can be used to validate PMF-based protein identification, it may not be practical when analyzing a large number of proteins and when high- throughput MS/MS instrumentation is not readily available. At present, a vast majority of proteomic studies employ PMF. However, there are huge disparities in criteria used to identify proteins using PMF. Therefore, to reduce incorrect protein identification using PMF, and also to increase confidence in PMF-based protein identification without accompanying MS/MS analysis, definitive guiding principles are essential. To this end, we propose a value-based scoring system that provides guidance on evaluating when PMF-based protein identification can be deemed sufficient without accompanying amino acid sequence data from MS/MS analysis.     

Using proteomics to mine genome sequences

We present a method for mining unannotated or annotated genome sequences with proteomic data to identify open reading frames. The region of a genome coding for a protein sequence is identified by using information from the analysis of proteins and peptides with MALDI-TOF mass spectrometry. The raw genome sequence or any unassembled contigs of an organism are theoretically cleaved into a number of equal sized but overlapping fragments, and these are then translated in all six frames into a series of virtual proteins. Each virtual protein is then subjected to a theoretical enzymatic digestion. Standard proteomic sample preparation methods are used to separate, array, and digest the proteins of interest to peptides. The masses of the resulting peptides are measured using mass spectrometry and compared to the theoretical peptide masses of the virtual proteins. The region of the genome responsible for coding for a particular protein can then be identified when there are a large number of hits between peptides from the protein and peptides from the virtual protein. The method makes no assumptions about the location of a protein in a particular gene sequence or the positions or types of start and stop codons. To illustrate this approach, all 773 proteins of Pseudomonas aeruginosa contained in SWISS-PROT were used to theoretically test the method and optimize parameters. Increasing the size of the virtual proteins results in an overall improvement in the ability to detect the coding region, at the cost of decreasing the sensitivity of the method for smaller proteins. Increasing the minimum number of matching peptides, lowering the mass error tolerance, or increasing the signal-to-noise ratio of the simulated mass spectrum, improves the ability to detect coding regions. The method is further demonstrated on experimental data from Mycobacterium tuberculosis and is also shown to work with eukaryotic organisms (e.g., Homo sapiens).     

The power and the limitations of cross-species protein identification by mass spectrometry-driven sequence similarity searches

Mass spectrometry-driven BLAST (MS BLAST) is a database search protocol for identifying unknown proteins by sequence similarity to homologous proteins available in a database. MS BLAST utilizes redundant, degenerate, and partially inaccurate peptide sequence data obtained by de novo interpretation of tandem mass spectra and has become a powerful tool in functional proteomic research. Using computational modeling, we evaluated the potential of MS BLAST for proteome-wide identification of unknown proteins. We determined how the success rate of protein identification depends on the full-length sequence identity between the queried protein and its closest homologue in a database. We also estimated phylogenetic distances between organisms under study and related reference organisms with completely sequenced genomes that allow substantial coverage of unknown proteomes.     

Tangible benefits of the aphid Acyrthosiphon pisum genome sequencing for aphid proteomics: Enhancements in protein identification and data validation for homology-based proteomics

Homology-driven proteomics promises to reveal functional biology in insects with sparse genome sequence information. A proteomics study comparing plant virus transmission competent and refractive genotypes of the aphid Schizaphis graminum isolated numerous candidate proteins involved in virus transmission, but limited genome sequence information hampered their identification. The complete genome of the pea aphid, Acyrthosiphon pisum, released in 2008, enabled us to double the number of protein identifications beyond what was possible using available EST libraries and other insect sequences. This was concomitant with a dramatic increase of the number of MS and MS/MS peptide spectra matching the genome-derived protein sequence. LC-MS/MS proved to be the most robust method of peptide detection. Cross-matching spectral data to multiple EST sequences and error tolerant searching to identify amino acid substitutions enhanced the percent coverage of the Schizaphis graminum proteins. 2-D electrophoresis provided the protein pI and MW which enabled the refinement of the candidate protein selection and provided a measure of protein abundance when coupled to the spectral data. Thus, the homology-based proteomics pipeline for insects should include efforts to maximize the number of peptide matches to the protein to increase certainty in protein identification and relative protein abundance.     

Protein identification from two-dimensional gel electrophoresis analysis of Klebsiella pneumoniae by combined use of mass spectrometry data and raw genome sequences

Separation of proteins by two-dimensional gel electrophoresis (2-DE) coupled with identification of proteins through peptide mass fingerprinting (PMF) by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is the widely used technique for proteomic analysis. This approach relies, however, on the presence of the proteins studied in public-accessible protein databases or the availability of annotated genome sequences of an organism. In this work, we investigated the reliability of using raw genome sequences for identifying proteins by PMF without the need of additional information such as amino acid sequences. The method is demonstrated for proteomic analysis of Klebsiella pneumoniae grown anaerobically on glycerol. For 197 spots excised from 2-DE gels and submitted for mass spectrometric analysis 164 spots were clearly identified as 122 individual proteins. 95% of the 164 spots can be successfully identified merely by using peptide mass fingerprints and a strain-specific protein database (ProtKpn) constructed from the raw genome sequences of K. pneumoniae. Cross-species protein searching in the public databases mainly resulted in the identification of 57% of the 66 high expressed protein spots in comparison to 97% by using the ProtKpn database. 10 dha regulon related proteins that are essential for the initial enzymatic steps of anaerobic glycerol metabolism were successfully identified using the ProtKpn database, whereas none of them could be identified by cross-species searching. In conclusion, the use of strain-specific protein database constructed from raw genome sequences makes it possible to reliably identify most of the proteins from 2-DE analysis simply through peptide mass fingerprinting.     

Proteome characterization of the unsequenced psychrophile Pedobacter cryoconitis using 15N metabolic labeling, tandem mass spectrometry, and a new bioinformatic workflow

Organisms without a sequenced genome and lacking a complete protein database encounter an added level of complexity to protein identification and quantitation. De novo sequencing, new bioinformatics tools, and mass spectrometry (MS) techniques allow for advances in this area. Here, the proteomic characterization of an unsequenced psychrophilic bacterium, Pedobacter cryoconitis, is presented employing a novel workflow based on (15) N metabolic labelling, 2DE, MS/MS, and bioinformatics tools. Two bioinformatics pipelines, based on nitrogen constraint (N-constraint), ortholog searching, and de novo peptide sequencing with N-constraint similarity database search, are compared based on proteome coverage and throughput. Results demonstrate the effect of different growth temperatures (1°C, 20°C) and different carbon sources (glucose, maltose) on the proteome. Seventy-six and 69 proteins were identified and validated from the glucose- and maltose-grown bacterium, respectively, from which 21 and 22 were differentially expressed at different growth temperatures. Differentially expressed proteins are involved in stress response and carbohydrate metabolism, with higher expression at 20°C than at 1°C, while antioxidants were upregulated at 1°C. This study provides an alternative workflow to identify, validate, and quantify proteins from unsequenced organisms distantly related to other species in the protein database. Furthermore, it provides further understanding on bacterial adaptation mechanisms to cold environments, and a comparative proteomic analyses with other psychrophilic microorganisms.     

Aligning the proteome and genome of the silkworm, Bombyx mori

A technology of mass spectrometry (MS) was used in this study for the large-scale proteomic identification and verification of protein-encoding genes present in the silkworm (Bombyx mori) genome. Peptide sequences identified by MS were compared with those from an open reading frame (ORF) library of the B. mori genome and a cDNA library, to validate the coding attributes of ORFs. Two databases were created. The first was based on a 9× draft sequence of the silkworm genome and contained 14,632 putative proteins. The second was based on a B. mori pupal cDNA library containing 3,187 putative proteins of at least 30 amino acid residues in length. A total of 81,000 peptide sequences with a threshold score of 60% were generated by the MS/MS analysis, and 55,400 of these were chosen for a sequence alignment. By searching these two databases, 6,649 and 250 proteins were matched, which accounted for approximately 45.4% and 7.8% of the peptide sequences and putative proteins, respectively. Further analyses carried out by several bioinformatic tools suggested that the matches included proteins with predicted transmembrane domains (1,393) and preproteins with a signal peptide (976). These results provide a fundamental understanding of the expression and function of silkworm proteins.     

Identification of a functionally relevant signal peptide of mouse ficolin A

Mouse ficolin A is a plasma protein with lectin activity, and plays a role in host defense by binding carbohydrates, especially GlcNAc, on microorganisms. The ficolin A subunit consists of an N-terminal signal peptide, a collagen-like domain, and a C-terminal fibrinogen-like domain. In this study, we show that ficolin A can be synthesized and oligomerized in a cell and secreted into culture medium. We also identify a functionally relevant signal peptide of ficolin A by using MS/MS analysis to determine the N-terminal sequence of secreted ficolin A. When the signal peptide of mouse ficolin A was fused with enhanced green fluorescent protein (EGFP), EGFP was released into HEK 293 cell medium, suggesting that the signal peptide can efficiently direct ficolin A secretion. Moreover, our results suggest that the signal peptide of ficolin A has potential application for the production of useful secretory proteins.     

Osteogenic differentiation of adipose derived stem cells promoted by overexpression of osterix

Secreted proteins, which may be involved in the regulation of various biological processes, are the potential targets for diagnosis and treatment of diverse diseases. In this study, to identify the human hepatoma HepG2 cells-derived secreted proteins more extensively, we applied the protein sample preparations using the combinations of denaturation methods and molecular-mass cutoff via ultrafiltration to the two-dimensional liquid chromatography coupled with tandem mass spectrometry (2D LC–MS/MS) analysis. We were able to identify a total of 86 proteins containing widely known secreted proteins of HepG2 such as alpha-fetoprotein, of which 73 proteins including 27 signal peptide-containing proteins have never been reported to be secreted from HepG2 cells in other proteomic studies. Among the identified signal peptide-containing proteins, ten proteins such as growth differentiation factor 15, osteopontin and stanniocalcin 2 were discovered as new secreted proteins of HepG2 cells. These observations suggest that the combinations of different sample preparation methods and 2D LC–MS/MS analysis are useful for identifying a wider range of low-abundance proteins and that the secreted proteins from HepG2 identified in this study may be useful as liver-specific biomarkers for diagnosis and treatment.     

Characterization of the mouse brain proteome using global proteomic analysis complemented with cysteinyl-peptide enrichment

We report a global proteomic approach for analyzing brain tissue and for the first time a comprehensive characterization of the whole mouse brain proteome. Preparation of the whole brain sample incorporated a highly efficient cysteinyl-peptide enrichment (CPE) technique to complement a global enzymatic digestion method. Both the global and the cysteinyl-enriched peptide samples were analyzed by SCX fractionation coupled with reversed phase LC-MS/MS analysis. A total of 48,328 different peptides were confidently identified (>98% confidence level), covering 7792 nonredundant proteins ( approximately 34% of the predicted mouse proteome). A total of 1564 and 1859 proteins were identified exclusively from the cysteinyl-peptide and the global peptide samples, respectively, corresponding to 25% and 31% improvements in proteome coverage compared to analysis of only the global peptide or cysteinyl-peptide samples. The identified proteins provide a broad representation of the mouse proteome with little bias evident due to protein pI, molecular weight, and/or cellular localization. Approximately 26% of the identified proteins with gene ontology (GO) annotations were membrane proteins, with 1447 proteins predicted to have transmembrane domains, and many of the membrane proteins were found to be involved in transport and cell signaling. The MS/MS spectrum count information for the identified proteins was used to provide a measure of relative protein abundances. The mouse brain peptide/protein database generated from this study represents the most comprehensive proteome coverage for the mammalian brain to date, and the basis for future quantitative brain proteomic studies using mouse models. The proteomic approach presented here may have broad applications for rapid proteomic analyses of various mouse models of human brain diseases.     

Proteomic study of the Arabidopsis thaliana chloroplastic envelope membrane utilizing alternatives to traditional two-dimensional electrophoresis

With the completion of the sequencing of the Arabidopsis genome and with the significant increase in the amount of other plant genome and expressed sequence tags (ESTs) data, plant proteomics is rapidly becoming a very active field. We have pursued a high-throughput mass spectrometry-based proteomics approach to identify and characterize membrane proteins localized to the Arabidopsis thaliana chloroplastic envelope membrane. In this study, chloroplasts were prepared from plate- or soil-grown Arabidopsis plants using a novel isolation procedure, and "mixed" envelopes were subsequently isolated using sucrose step gradients. We applied two alternative methodologies, off-line multidimensional protein identification technology (Off-line MUDPIT) and one-dimensional (1D) gel electrophoresis followed by proteolytic digestion and liquid chromatography coupled with tandem mass spectrometry (Gel-C-MS/MS), to identify envelope membrane proteins. This proteomic study enabled us to identify 392 nonredundant proteins.     

Proteomics of industrial fungi: trends and insights for biotechnology

Filamentous fungi are widely known for their industrial applications, namely, the production of food-processing enzymes and metabolites such as antibiotics and organic acids. In the past decade, the full genome sequencing of filamentous fungi increased the potential to predict encoded proteins enormously, namely, hydrolytic enzymes or proteins involved in the biosynthesis of metabolites of interest. The integration of genome sequence information with possible phenotypes requires, however, the knowledge of all the proteins in the cell in a system-wise manner, given by proteomics. This review summarises the progress of proteomics and its importance for the study of biotechnological processes in filamentous fungi. A major step forward in proteomics was to couple protein separation with high-resolution mass spectrometry, allowing accurate protein quantification. Despite the fact that most fungal proteomic studies have been focused on proteins from mycelial extracts, many proteins are related to processes which are compartmentalised in the fungal cell, e.g. β-lactam antibiotic production in the microbody. For the study of such processes, a targeted approach is required, e.g. by organelle proteomics. Typical workflows for sample preparation in fungal organelle proteomics are discussed, including homogenisation and sub-cellular fractionation. Finally, examples are presented of fungal organelle proteomic studies, which have enlarged the knowledge on areas of interest to biotechnology, such as protein secretion, energy production or antibiotic biosynthesis.     

Large-scale identification of Caenorhabditis elegans proteins by multidimensional liquid chromatography-tandem mass spectrometry

A proteome of a model organism, Caenorhabditis elegans, was analyzed by an integrated liquid chromatography (LC)-based protein identification system, which was constructed by microscale two-dimensional liquid chromatography (2DLC) coupled with electrospray ionization (ESI) tandem mass spectrometry (MS/MS) on a high-resolution hybrid mass spectrometer with an automated data analysis system. Soluble and insoluble protein fractions were prepared from a mixed growth phase culture of the worm C. elegans, digested with trypsin, and fractionated separately on the 2DLC system. The separated peptides were directly analyzed by on-line ESI-MS/MS in a data-dependent mode, and the resultant spectral data were automatically processed to search a genome sequence database, wormpep 66, for protein identification. The total number of proteins of the composite proteome identified in this method was 1,616, including 110 secreted/targeted proteins and 242 transmembrane proteins. The codon adaptation indices of the identified proteins suggested that the system could identify proteins of relatively low abundance, which are difficult to identify by conventional 2D-gel electrophoresis (GE) followed by an offline mass spectrometric analysis such as peptide mass fingerprinting. Among the approximately 5,400 peptides assigned in this study, many peptides with post-translational modifications, such as N-terminal acetylation and phosphorylation, were detected. This expression profile of C. elegans, containing 571 hypothetical gene products, will serve as the basic data of a major proteome set expressed in the worm.