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The field of proteomics involves the characterization of the peptides and proteins expressed in a cell under specific conditions. Proteomics has made rapid advances in recent years following the sequencing of the genomes of an increasing number of organisms. A prominent technology for high throughput proteomics analysis is the use of liquid chromatography coupled to Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR-MS). Meaningful biological conclusions can best be made when the peptide identities returned by this technique are accompanied by measures of accuracy and confidence. After a tryptically digested protein mixture is analyzed by LC-FTICR-MS, the observed masses and normalized elution times of the detected features are statistically matched to the theoretical masses and elution times of known peptides listed in a large database. The probability of matching is estimated for each peptide in the reference database using statistical classification methods assuming bivariate Gaussian probability distributions on the uncertainties in the masses and the normalized elution times. A database of 69,220 features from 32 LC-FTICR-MS analyses of a tryptically digested bovine serum albumin (BSA) sample was matched to a database populated with 97% false positive peptides. The percentage of high confidence identifications was found to be consistent with other database search procedures. BSA database peptides were identified with high confidence on average in 14.1 of the 32 analyses. False positives were identified on average in just 2.7 analyses. Using a priori probabilities that contrast peptides from expected and unexpected proteins was shown to perform better in identifying target peptides than using equally likely a priori probabilities. This is because a large percentage of the target peptides were similar to unexpected peptides which were included to be false positives. The use of triplicate analyses with a "2 out of 3" reporting rule was shown to have excellent rejection of false positives.

Hodgkin lymphoma (HL) represents a category of lymphoid neoplasms with unique features, notably the usual scarcity of tumour cells in involved tissues. The most common subtype of classical HL, nodular sclerosis HL, characteristically comprises abundant fibrous tissue stroma. Little information is available about the protein composition of the stromal environment from HL. Moreover, the identification of valid protein targets, specifically and abundantly expressed in HL, would be of utmost importance for targeted therapies and imaging, yet the biomarkers must necessarily be accessible from the bloodstream. To characterize HL stroma and to identify potentially accessible proteins, we used a chemical proteomic approach, consisting in the labelling of accessible proteins and their subsequent purification and identification by mass spectrometry. We performed an analysis of potentially accessible proteins in lymph node biopsies from HL and reactive lymphoid tissues, and in total, more than 1400 proteins were identified in 7 samples. We have identified several extracellular matrix proteins overexpressed in HL, such as versican, fibulin-1, periostin, and other proteins such as S100-A8. These proteins were validated by immunohistochemistry on a larger series of biopsy samples, and bear the potential to become targets for antibody-based anti-cancer therapies.

The sea urchin embryo has been an important model organism in developmental biology for more than a century. This is due to its relatively simple construction, translucent appearance, and the possibility to follow the fate of individual cells as development to the pluteus larva proceeds. Because the larvae contain tiny calcitic skeletal elements, the spicules, they are also important model organisms for biomineralization research. Similar to other biominerals the spicule contains an organic matrix, which is thought to play an important role in its formation. However, only few spicule matrix proteins were identified previously. Using mass spectrometry-based methods we have identified 231 proteins in the matrix of the S. purpuratus spicule matrix. Approximately two thirds of the identified proteins are either known or predicted to be extracellular proteins or transmembrane proteins with large ectodomains. The ectodomains may have been solubilized by partial proteolysis and subsequently integrated into the growing spicule. The most abundant protein of the spicule matrix is SM50. SM50-related proteins, SM30-related proteins, MSP130 and related proteins, matrix metalloproteases and carbonic anhydrase are among the most abundant components. The spicule matrix is a relatively complex mixture of proteins not only containing matrix-specific proteins with a function in matrix assembly or mineralization, but also: 1) proteins possibly important for the formation of the continuous membrane delineating the mineralization space; 2) proteins for secretory processes delivering proteinaceous or non-proteinaceous precursors; 3) or proteins reflecting signaling events at the cell/matrix interface. Comparison of the proteomes of different skeletal matrices allows prediction of proteins of general importance for mineralization in sea urchins, such as SM50, SM30-E, SM29 or MSP130. The comparisons also help point out putative tissue-specific proteins, such as tooth phosphodontin or specific spicule matrix metalloproteases of the MMP18/19 group. Furthermore, the direct sequence analysis of peptides by MS/MS validates many predicted genes and confirms the existence of the corresponding proteins.

Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS) is a frequently used technique for cancer biomarker research. The specificity of biomarkers detected by SELDI can be influenced by concomitant inflammation. This study aimed to increase detection accuracy using a two-stage analysis process. Sera from 118 lung cancer patients, 72 healthy individuals, and 31 patients with inflammatory disease were randomly divided into training and testing groups by 3:2 ratio. In the training group, the traditional method of using SELDI profile analysis to directly distinguish lung cancer patients from sera was used. The two-stage analysis of distinguishing the healthy people and non-healthy patients (1st-stage) and then differentiating cancer patients from inflammatory disease patients (2nd-stage) to minimize the influence of inflammation was validated in the test group. In the test group, the one-stage method had 87.2% sensitivity, 37.5% specificity, and 64.4% accuracy. The two-stage method had lower sensitivity (> 70.1%) but statistically higher specificity (80%) and accuracy (74.7%). The predominantly expressed protein peak at 11480 Da was the primary splitter regardless of one- or two-stage analysis. This peak was suspected to be SAA (Serum Amyloid A) due to the similar m/z countered around this area. This hypothesis was further tested using an SAA ELISA assay. Inflammatory disease can severely interfere with the detection accuracy of SELDI profiles for lung cancer. Using a two-stage training process will improve the specificity and accuracy of detecting lung cancer.

The cytokine MIF (Macrophage Migration Inhibitory Factor) has diverse physiological roles and is present at elevated concentrations in numerous disease states. However, its molecular heterogeneity has not been previously investigated in biological samples. Mass Spectrometric Immunoassay (MSIA) may help elucidate MIF post-translational modifications existing in vivo and provide additional clarity regarding its relationship to diverse pathologies. In this work, we have developed and validated a fully quantitative MSIA assay for MIF, and used it in the discovery and quantification of different proteoforms of MIF in serum samples, including cysteinylated and glycated MIF. The MSIA assay had a linear range of 1.56-50 ng/mL, and exhibited good precision, linearity, and recovery characteristics. The new assay was applied to a small cohort of human serum samples, and benchmarked against an MIF ELISA assay. The quantitative MIF MSIA assay provides a sensitive, precise and high throughput method to delineate and quantify MIF proteoforms in biological samples.

Protection of pigs by vaccination against Actinobacillus pleuropneumoniae, the causative agent of porcine pleuropneumonia, is hampered by the presence of 15 different serotypes. A DIVA subunit vaccine comprised of detergent-released proteins from A. pleuropneumoniae serotypes 1, 2 and 5 has been developed and shown to protect pigs from clinical symptoms upon homologous and heterologous challenge. This vaccine has not been characterized in-depth so far. Thus we performed i) mass spectrometry in order to identify the exact protein content of the vaccine and ii) cross-serotype 2-D immunoblotting in order to discover cross-reactive antigens. By these approaches we expected to gain results enabling us to argue about the reasons for the efficacy of the analyzed vaccine. We identified 75 different proteins in the vaccine. Using the PSORTb algorithm these proteins were classified according to their cellular localization. Highly enriched proteins are outer membrane-associated lipoproteins like OmlA and TbpB, integral outer membrane proteins like FrpB, TbpA, OmpA1, OmpA2, HgbA and OmpP2, and secreted Apx toxins. The subunit vaccine also contained large amounts of the ApxIVA toxin so far thought to be expressed only during infection. Applying two-dimensional difference gel electrophoresis (2-D DIGE) we showed different isoforms and variations in expression levels of several proteins among the strains used for vaccine production. For detection of cross-reactive antigens we used detergent released proteins of serotype 7. Sera of pigs vaccinated with the detergent-released proteins of serotypes 1, 2, and 5 detected seven different proteins of serotype 7, and convalescent sera of pigs surviving experimental infection with serotype 7 reacted with 13 different proteins of the detergent-released proteins of A. pleuropneumoniae serotypes 1, 2, and 5. A detergent extraction-based subunit vaccine of A. pleuropneumoniae was characterized by mass spectrometry. It contained a large variety of immunogenic and virulence associated proteins, among them the ApxIVA toxin. The identification of differences in expression as well as isoform variation between the serotypes implied the importance of combining proteins of different serotypes for vaccine generation. This finding was supported by immunoblotting showing the induction of cross-reactive antibodies against several surface associated proteins in immunized animals.

Liver models that closely mimic the in vivo microenvironment are useful for understanding liver functions, capabilities, and intercellular communication processes. Three-dimensional (3D) liver models assembled using hepatocytes and liver sinusoidal endothelial cells (LSECs) separated by a polyelectrolyte multilayer (PEM) provide a functional system while also permitting isolation of individual cell types for proteomic analyses. To better understand the mechanisms and processes that underlie liver model function, hepatocytes were maintained as monolayers and 3D PEM-based formats in the presence or absence of primary LSECs. The resulting hepatocyte proteomes, the proteins in the PEM, and extracellular levels of urea, albumin and glucose after three days of culture were compared. All systems were ketogenic and found to release glucose. The presence of the PEM led to increases in proteins associated with both mitochondrial and peroxisomal-based β-oxidation. The PEMs also limited production of structural and migratory proteins associated with dedifferentiation. The presence of LSECs increased levels of Phase I and Phase II biotransformation enzymes as well as several proteins associated with the endoplasmic reticulum and extracellular matrix remodeling. The proteomic analysis of the PEMs indicated that there was no significant change after three days of culture. These results are discussed in relation to liver model function. Heterotypic cell-cell and cell-ECM interactions exert different effects on hepatocyte functions and phenotypes.