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PubMed2006

Analysis of chicken serum proteome and differential protein expression during development in single-comb White Leghorn hens.

Serum is believed to harbor thousands of distinct proteins that are either actively secreted or leak from various blood cells or tissues. Exploring protein composition in serum may accelerate the discovery of novel protein biomarkers for specific economic traits in livestock species. This study analyzed serum protein composition to establish a 2-DE reference map, and monitored protein dynamics of single-comb White Leghorn hens at 8, 19 and 23 weeks after hatching. A total of 119 CBB-stained and 315 silver-stained serum protein spots were analyzed by MALDI-TOF MS. Of these, 98 CBB-stained and 94 silver-stained protein spots were significantly matched to existing chicken proteins. The identified spots represented 30 distinctive proteins in the serum of laying hens. To compare protein expression during development, expression levels of 47 protein spots were quantified by relative spot volume with Melanie 3 software. Ten protein spots increased and 3 protein spots decreased as hen age increased. Previous research has suggested that some of these proteins play critical roles in egg production. The differentially expressed proteins with unknown identities will be valuable candidates for further explorations of their roles in egg production of laying hens.

Age Factors↗
PubMed2005

Classification and identification of bacteria using mass spectrometry-based proteomics.

Timely classification and identification of bacteria is of vital importance in many areas of public health. Mass spectrometry-based methods provide an attractive alternative to well-established microbiologic procedures. Mass spectrometry methods can be characterized by the relatively high speed of acquiring taxonomically relevant information. Gel-free mass spectrometry proteomics techniques allow for rapid fingerprinting of bacterial proteins using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry or, for high-throughput sequencing of peptides from protease-digested cellular proteins, using mass analysis of fragments from collision-induced dissociation of peptide ions. The latter technique uses database searching of product ion mass spectra. A database contains a comprehensive list of protein sequences translated from protein-encoding open reading frames found in bacterial genomes. The results of such searches allow the assignment of experimental peptide sequences to matching theoretical bacterial proteomes. Phylogenetic profiles of sequenced peptides are then used to create a matrix of sequence-to-bacterium assignments, which are analyzed using numerical taxonomy tools. The results thereof reveal the relatedness between bacteria, and allow the taxonomic position of an investigated strain to be inferred.

Animals↗
PubMed2005

The human plasma proteome: analysis of Chinese serum using shotgun strategy.

We have investigated the serum proteome of Han-nationality Chinese by using shotgun strategy. A complete proteomics analysis was performed on two reference specimens from a total of 20 healthy donors, in which each sample was made from ten-pooled male or female serum, respectively. The methodology used encompassed (1) removal of six high-abundant proteins; (2) tryptic digestion of low- and high-abundant proteins of serum; (3) separation of peptide mixture by RP-HPLC followed by ESI-MS/MS identification. A total of 944 nonredundant proteins were identified under a stringent filter condition (X(corr) > or = 1.9, > or = 2.2, and > or = 3.75, < or = C(n) > or = 0.1, and R(sp) > or = 4.0) in both pooled male and female samples, in which 594 and 622 entire proteins were found, respectively. Compared with the total 3020 protein identifications confirmed by more than one laboratory or more than one specimen in HUPO Plasma Proteome Project (PPP) participating laboratories recently, 206 proteins were identified with at least two distinct peptides per protein and 185 proteins were considered as high-confidence identification. Moreover, some lower abundance serum proteins (ng/mL range) were detected, such as complement C5 and CA125, routinely used as an ovarian cancer marker in plasma and serum. The resulting nonredundant list of serum proteins would add significant information to the knowledge base of human plasma proteome and facilitate disease markers discovery.

Biomarkers↗
PubMed2006

Two-dimensional gel electrophoresis as tool for proteomics studies in combination with protein identification by mass spectrometry.

The proteome analysis by 2-DE is one of the most potent methods of analyzing the complete proteome of cells, cell lines, organs and tissues in proteomics studies. It allows a fast overview of changes in cell processes by analysis of the entire protein extracts in any biological and medical research projects. New instrumentation and advanced technologies provide proteomics studies in a wide variety of biological and biomedical questions. Proteomics work is being applied to study antibiotics-resistant strains and human tissues of various brain, lung, and heart diseases. It cumulated in the identification of antigens for the design of new vaccines. These advances in proteomics have been possible through the development of advanced high-resolution 2-DE systems allowing resolution of up to 10 000 protein spots of entire cell lysates in combination with protein identification by new highly sensitive mass spectrometric techniques. The present technological achievements are suited for a high throughput screening of different cell situations. Proteomics may be used to investigate the health effects of radiation and electromagnetic field to clarify possible dangerous alterations in human beings.

Animals↗
PubMed2006

Proteomic analysis of a fraction enriched in cell envelope proteins of Acinetobacter baumannii.

Acinetobacter baumannii is a multiresistant opportunistic nosocomial pathogen. A protein fraction was purified and analyzed by 2-DE. Twenty-nine major protein spots were selected for protein identification using trypsin digestion and MS analysis. As the A. baumannii genome has not yet been described, protein identification was performed by homology with other Acinetobacter species in the NCBi database. We identified ribosomal proteins, chaperones, elongation factors and outer membrane proteins (Omp), such as OmpA and the 33-36-kDa OMP. Proteomic analysis of A. baumannii provides a platform for further studies in antimicrobial resistance.

Acinetobacter baumannii↗
PubMed2004

Divinyl sulfone as a postdigestion modifier for enhancing the a(1) Ion in MS/MS and postsource decay: potential applications in proteomics.

Divinyl sulfone reacts at pH 8-9 with the alpha-amino groups of N-terminal residues, proline, the epsilon-amino groups of lysine, and the histidine side chains of peptides. This reaction leads to great enhancement of the abundance of the normally weak or missing "a(1)" fragment ion in MS/MS analysis defining the N-terminal residue of a peptide in a digest. This provides "one-step Edman-like" information that, together with a fairly accurately determined mass, often enables one to correctly identify a protein or family of proteins. The applicability of this procedure in proteomics was demonstrated with several peptides and tryptic digests of protein mixtures by LC-MS/MS experiments using a QTOF and MALDI-PSD analyses. Advantages of this approach are its simple chemistry, retention of charge multiplicity, and possibly, shortening of database search time. Used with other MS/MS data, it provides higher confidence in the scores and identification of a protein found in peptide mass fingerprinting. Moreover, this approach has an advantage in "de novo" sequencing due to its ability to decipher the first amino acid of a peptide whose information is normally unavailable in MS/MS spectra.

Amino Acid Sequence↗
PubMed2006

Trade-off between high sensitivity and increased potential for false positive peptide sequence matches using a two-dimensional linear ion trap for tandem mass spectrometry-based proteomics.

Two-dimensional linear ion trap mass spectrometers are rapidly becoming the new workhorse instruments for shotgun proteomic analysis of complex peptide mixtures. The objective of this study was to compare the potential for false positive peptide sequence matches between a two-dimensional ion trap instrument and a traditional, three-dimensional ion trap instrument. Through the comparative analysis of a complex protein sample, we found that in order to minimize false positive sequence matches, sequence match scoring criteria must be more stringent for data from the two-dimensional ion trap compared to the three-dimensional ion trap data. Given this increased potential for false positives, we also investigated two potential filtering strategies to reduce the false positive matches for data derived from the two-dimensional ion trap, including trypsin enzyme cleavage filtering, and the addition of peptide physicochemical information as a constraint, specifically peptide isoelectric point. The results described here provide a cautionary tale to researchers, demonstrating the need for careful analysis of MS/MS data from this new class of ion trap instruments, as well as the effectiveness of trypsin enzyme cleavage filtering and peptide pI information in maximizing high confidence protein identifications from this powerful proteomic instrumentation.

Amino Acid Sequence↗
PubMed2006

Integrative investigation of metabolic and transcriptomic data.

BACKGROUND: New analysis methods are being developed to integrate data from transcriptome, proteome, interactome, metabolome, and other investigative approaches. At the same time, existing methods are being modified to serve the objectives of systems biology and permit the interpretation of the huge datasets currently being generated by high-throughput methods. RESULTS: Transcriptomic and metabolic data from chemostat fermentors were collected with the aim of investigating the relationship between these two data sets. The variation in transcriptome data in response to three physiological or genetic perturbations (medium composition, growth rate, and specific gene deletions) was investigated using linear modelling, and open reading-frames (ORFs) whose expression changed significantly in response to these perturbations were identified. Assuming that the metabolic profile is a function of the transcriptome profile, expression levels of the different ORFs were used to model the metabolic variables via Partial Least Squares (Projection to Latent Structures--PLS) using PLS toolbox in Matlab. CONCLUSION: The experimental design allowed the analyses to discriminate between the effects which the growth medium, dilution rate, and the deletion of specific genes had on the transcriptome and metabolite profiles. Metabolite data were modelled as a function of the transcriptome to determine their congruence. The genes that are involved in central carbon metabolism of yeast cells were found to be the ORFs with the most significant contribution to the model.

Algorithms↗
PubMed2006

Proteomic analysis of Dictyostelium discoideum.

The social amoeba Dictyostelium discoideum is already known as a model organism for a variety of cellular and molecular studies. Now that the genome sequencing project has been completed and different tools with which to overexpress or knock out genes are available, this species has also moved into the spotlight of functional genomics studies. Consequently, this genomic sequence information can now be exploited to realize D. discoideum proteomics projects. Here, we present validated protocols adapted for analysis of the D. discoideum proteome. The workflow described in this chapter comprises two-dimensional polyacrylamide gel electrophoresis for protein separation and peptide mass fingerprint (matrix-assisted laser desorption/ionization time-of-flight mass spectrometry) for protein identification.

Animals↗
PubMed2006

Data merging for integrated microarray and proteomic analysis.

The functioning of even a simple biological system is much more complicated than the sum of its genes, proteins and metabolites. A premise of systems biology is that molecular profiling will facilitate the discovery and characterization of important disease pathways. However, as multiple levels of effector pathway regulation appear to be the norm rather than the exception, a significant challenge presented by high-throughput genomics and proteomics technologies is the extraction of the biological implications of complex data. Thus, integration of heterogeneous types of data generated from diverse global technology platforms represents the first challenge in developing the necessary foundational databases needed for predictive modelling of cell and tissue responses. Given the apparent difficulty in defining the correspondence between gene expression and protein abundance measured in several systems to date, how do we make sense of these data and design the next experiment? In this review, we highlight current approaches and challenges associated with integration and analysis of heterogeneous data sets, focusing on global analysis obtained from high-throughput technologies.

Animals↗
PubMed2006

Improving the accuracy of protein secondary structure prediction using structural alignment.

BACKGROUND: The accuracy of protein secondary structure prediction has steadily improved over the past 30 years. Now many secondary structure prediction methods routinely achieve an accuracy (Q3) of about 75%. We believe this accuracy could be further improved by including structure (as opposed to sequence) database comparisons as part of the prediction process. Indeed, given the large size of the Protein Data Bank (>35,000 sequences), the probability of a newly identified sequence having a structural homologue is actually quite high. RESULTS: We have developed a method that performs structure-based sequence alignments as part of the secondary structure prediction process. By mapping the structure of a known homologue (sequence ID >25%) onto the query protein's sequence, it is possible to predict at least a portion of that query protein's secondary structure. By integrating this structural alignment approach with conventional (sequence-based) secondary structure methods and then combining it with a "jury-of-experts" system to generate a consensus result, it is possible to attain very high prediction accuracy. Using a sequence-unique test set of 1644 proteins from EVA, this new method achieves an average Q3 score of 81.3%. Extensive testing indicates this is approximately 4-5% better than any other method currently available. Assessments using non sequence-unique test sets (typical of those used in proteome annotation or structural genomics) indicate that this new method can achieve a Q3 score approaching 88%. CONCLUSION: By using both sequence and structure databases and by exploiting the latest techniques in machine learning it is possible to routinely predict protein secondary structure with an accuracy well above 80%. A program and web server, called PROTEUS, that performs these secondary structure predictions is accessible at http://wishart.biology.ualberta.ca/proteus. For high throughput or batch sequence analyses, the PROTEUS programs, databases (and server) can be downloaded and run locally.

Algorithms↗
PubMed2006

Djinn Lite: a tool for customised gene transcript modelling, annotation-data enrichment and exploration.

BACKGROUND: There is an ever increasing rate of data made available on genetic variation, transcriptomes and proteomes. Similarly, a growing variety of bioinformatic programs are becoming available from many diverse sources, designed to identify a myriad of sequence patterns considered to have potential biological importance within inter-genic regions, genes, transcripts, and proteins. However, biologists require easy to use, uncomplicated tools to integrate this information, visualise and print gene annotations. Integrating this information usually requires considerable informatics skills, and comprehensive knowledge of the data format to make full use of this information. Tools are needed to explore gene model variants by allowing users the ability to create alternative transcript models using novel combinations of exons not necessarily represented in current database deposits of mRNA/cDNA sequences. RESULTS: Djinn Lite is designed to be an intuitive program for storing and visually exploring of custom annotations relating to a eukaryotic gene sequence and its modelled gene products. In particular, it is helpful in developing hypothesis regarding alternate splicing of transcripts by allowing the construction of model transcripts and inspection of their resulting translations. It facilitates the ability to view a gene and its gene products in one synchronised graphical view, allowing one to drill down into sequence related data. Colour highlighting of selected sequences and added annotations further supports exploration, visualisation of sequence regions and motifs known or predicted to be biologically significant. CONCLUSION: Gene annotating remains an ongoing and challenging task that will continue as gene structures, gene transcription repertoires, disease loci, protein products and their interactions become more precisely defined. Djinn Lite offers an accessible interface to help accumulate, enrich, and individualize sequence annotations relating to a gene, its transcripts and translations. The mechanism of transcript definition and creation, and subsequent navigation and exploration of features, are very intuitive and demand only a short learning curve. Ultimately, Djinn Lite can form the basis for providing valuable clues to plan new experiments, providing storage of sequences and annotations for dedication to customised projects. The application is appropriate for Windows 98-ME-2000-XP-2003 operating systems.

Alternative Splicing↗
PubMed2005

Protein identification assisted by the prediction of retention time in liquid chromatography/tandem mass spectrometry.

Two-dimensional liquid chromatography (2D-LC) coupled on-line with electrospray ionization tandem mass spectrometry (2D-LC-ESI-MS/MS) is a new platform for analysis and identification of proteome. Peptides are separated by 2D-LC and then performed MS/MS analysis by tandem MS/MS. The MS/MS data are searched against database for protein identification. In one 2D-LC-ESI-MS/MS run, we obtained not only the structural information of peptides directly from MS/MS, but also the retention time of peptides eluted from LC. Information on the chromatographic behavior of peptides can assist protein identification in the new platform for proteomics. The retention time of the matching peptides of the identified protein was predicted by the hydrophobic contribute of each amino acid on reversed-phase liquid chromatography (RPLC). By using this strategy proteins were identified by four types of information: peptide mass fingerprinting (PMF), sequence query, and MS/MS ions searched and the predicted retention time. This additional information obtained from LC could assist protein identification with no extra experimental cost.

Amino Acid Sequence↗
PubMedDate not supplied

Experiments in searching small proteins in unannotated large eukaryotic genomes.

There is growing interest to use mass spectrometry data to search genome sequences directly. Previous work by other authors demonstrated that this approach is able to correct and complement available genome annotations. We discuss the practical difficulty of searching large eukaryotic genomes with peptide ion trap tandem mass spectra of small proteins (<40 kDa). The challenging problem of automatically identifying peptides that span across exon/intron boundaries is explored for the first time by using experimental data. In a human genome search, we find that roughly 30% of the peptides are missed, due to various reasons, compared to a Swiss-Prot search. We show that this percentage is significantly reduced with improved parent mass accuracy. We finally provide several examples of predicted gene structures that could be improved by proteomics data, in particular by peptides spanning across exon/intron boundaries.

Adult↗
PubMed2004

Technical aspects of functional proteomics in plants.

Since the completion of genome sequences of several organisms, attention has been focused to determine the function and functional network of proteins by proteome analysis. This analysis is achieved by separation and identification of proteins, determination of their function and functional network, and construction of an appropriate database. Many improvements in separation and identification of proteins, such as two-dimensional electrophoresis, nano-liquid chromatography and mass spectrometry, have rapidly been achieved. Some new techniques which include top-down mass spectrometry and tandem affinity purification have emerged. These techniques have provided the possibility of high-throughput analysis of function and functional network of proteins in plants. However, to cope with the huge information emerging from proteome analyses, more sophisticated techniques and software are essential. The development and adaptation of such techniques will ease analyses of protein profiling, identification of post-translational modifications and protein-protein interaction, which are vital for elucidation of the protein functions.

Databases, Protein↗
PubMed2004

Snake venomics: characterization of protein families in Sistrurus barbouri venom by cysteine mapping, N-terminal sequencing, and tandem mass spectrometry analysis.

The protein composition of the crude venom of Sistrurus barbouri was analyzed by two-dimensional sodium dodecyl sulfate polyacrylamide gel electrophoresis. Proteins were separated by reversed phase high-performance liquid chromatography and characterized by N-terminal sequence analysis. The molecular mass and number of cysteine residues of the purified proteins were determined by matrix-associated laser desorption/ionization-time of flight mass spectrometry. Selected protein bands were subjected to in-gel tryptic digestion and peptide mass fingerprinting. Analysis of the tandem mass spectrometry spectra of selected doubly-charged peptide ions was done by collision-induced dissociation in a quadrupole-linear ion trap instrument. Our results show that the venom proteome of the pigmy rattlesnake S. barbouri is composed of proteins belonging to a few protein families, which can be structurally characterized by their disulfide bond contents.

Amino Acid Sequence↗
PubMedDate not supplied

[The first protein map of Synechococcus sp. strain PCC 7942].

The first protein map was developed of Synechococcus sp. strain PCC 7942, a model organism for studies of photosynthesis, prokaryotic circadian rhythms, cell division, carbon-concentrating mechanisms, and adaptive responses to a variety of stresses. The proteome was analyzed by two-dimensional gel electrophoresis with subsequent MALDI-TOF mass spectroscopy and database analysis. Of the 140 analyzed protein spots, 110 were successfully identified as 62 different proteins, many of which occurred as multiple spots on the gel. The identified proteins participate in the major metabolic and cellular processes in cyanobacterial cells during the exponential growth phase. In addition, 14 proteins which were previously either unknown or considered to be hypothetical were shown to be true gene products in Synechococcus sp. strain PCC 7942. These results may be helpful for the annotation of the recently sequenced genome of this cyanobacterium, as well as for biochemical and physiological studies of Synechococcus.

Bacterial Proteins↗
PubMed2006

Expanding the protein catalogue in the proteome reference map of human breast cancer cells.

In this report we present a catalogue of 162 proteins (including isoforms and variants) identified in a prototype of proteomic map of breast cancer cells. This work represents the prosecution of previous studies describing the protein complement of breast cancer cells of the line 8701-BC, which has been well characterized for several parameters, providing to be a useful model for the study of breast cancer-associated candidate biomarkers. In particular, 110 spots were identified ex novo by PMF, or validated following previous gel matching identification method; 30 were identified by N-terminal microsequencing and the remaining by gel matching with maps available from our former work. As a consequence of the expanded number of proteins, we have updated our previous classification extending the number of protein groups from 4 to 13. In order to facilitate comparative proteome studies of different kinds of breast cancers, in this report we provide the whole complement of proteins so far identified and grouped into the new classification. A consistent number of them were not described before in other proteomic maps of breast cancer cells or tissues, and therefore they represent a valuable contribution for breast cancer protein databases and for future application in basic and clinical researches.

Biomarkers, Tumor↗