Virtual and experimental 2DE coupled with ESI LC-MS/MS was introduced to obtain better representation of the information about human proteome. The proteins from HEPG2 cells and human blood plasma were run by 2DE. After staining and protein spot identification by MALDI-TOF MS, the protein maps were generated. The experimental physicochemical parameters (pI/Mw) of the proteoforms further detected by ESI LC-MS/MS in these spots were obtained. Next, the theoretical pI and Mw of identified proteins were calculated using program Compute pI/Mw (http://web.expasy.org/compute_pi/pi_tool-doc.html). Accordingly, the relationship between theoretical and experimental parameters was analyzed, and the correlation plots were built. Additionally, virtual/experimental information about different protein species/proteoforms from the same genes was extracted. As it was revealed from the plots, the major proteoforms detected in HepG2 cell line have pI/Mw parameters similar to theoretical values. In opposite, the minor protein species have mainly very different from theoretical pI and Mw parameters. A similar situation was observed in plasma in much higher degree. It means that minor protein species are heavily modified in cell and even more in plasma proteome.
To obtain more information about human proteome, especially about proteoforms (protein species) coded by 18th chromosome, we separated proteins from human cancer cell line (HepG2) by two-dimensional gel electrophoresis (2DE). Initially, proteins in major spots were identified by MALDI-MS peptide mass fingerprinting. According to parameters (pI/Mw) of identified proteins the gel was calibrated. Using this calibrated gel, a virtual 2D map of proteoforms coded by Chromosome 18 was constructed. Next, the produced gel was divided into 96 sections with determined coordinates. Each section was cut, shredded, and treated by trypsin according to mass-spectrometry protocol. After protein identification by shotgun mass spectrometry using ESI LC-MS/MS, a list of 20 462 proteoforms (product of 3774 genes) was generated. Among them, 165 proteoforms are representing 39 genes of 18th chromosome. The 3D graphs showing the distribution of different proteoforms from the same gene in 2D map were generated. This is a first step in creation of 2DE-based knowledge database of proteins coded by 18th chromosome.
Identification and quantitative analysis of different proteoforms (protein species) presented in a cell line generated from high grade glioblastoma was performed using two-dimensional electrophoresis (2DE), mass spectrometry (ESI LC-MS/MS), and immunodetection. A 2DE protein map containing an extensive data set comprising 937 spots with 1542 unique protein identifications (proteoforms) of 600 genes was obtained. Additionally, another set of experiments was performed where 16012 proteoforms coded by 4050 genes were identified by MS/MS according to their position in 96 gel sections (pixels). A special attention has been paid to the proteins that are the potential biomarkers of glioblastoma. The list of these biomarkers was compiled from literature. Next, we generated the graphs with theoretical and experimental information about proteoforms coded by the same gene. Such a virtualexperimental representation allowed better visualization of the state of these gene products. Many proteins, potential biomarkers of glioblastoma as well, are characterized by high numbers of protein species. We assume that these species could be a potential source of highly specific biomarkers of glioblastoma.
Haptoglobin (Hp) is a blood plasma glycoprotein that plays a critical role in tissue protection and the prevention of oxidative damage. Haptoglobin is an acute-phase protein, its concentration in plasma changes in pathology, and the test for its concentration is part of normal clinical practice. Haptoglobin is a conservative protein and is the subject of research as a potential biomarker of many diseases, including malignant neoplasms. The Human Hp gene is polymorphic and controls the synthesis of three major phenotypes—homozygous Hp1-1 and Hp2-2, and heterozygous Hp2-1, determined by a combination of allelic variants that are inherited. Numerous studies indicate that the phenotype of haptoglobin can be used to judge the individual’s predisposition to various diseases. In addition, Hp undergoes various post-translational modifications (PTMs). Glioblastoma multiform (GBM) is the most malignant primary brain tumor. In our study, we have analyzed the state of Hp proteoforms in plasma and cells using 1D (SDS-PAGE) and 2D electrophoresis (2DE) with the following mass spectrometry (LC ES-MS/MS) or Western blotting. We found that the levels of α2- and β-chain proteoforms are up-regulated in the plasma of GBM patients. An unprocessed form of Hp2-2 (PreHp2-2, zonulin) with unusual biophysical parameters (pI/Mw) was also detected in the plasma of GBM patients and glioblastoma cells. Altogether, this data shows the possibility to use proteoforms of haptoglobin as a potential GBM-specific plasma biomarker.
The human proteome is composed of a diverse and heterogeneous range of gene products/proteoforms/protein species. Because of the growing amount of information about proteoforms generated by different methods, we need a convenient approach to make an inventory of the data. Here, we present a database of proteoforms that is based on information obtained by separation of proteoforms using 2DE followed by shotgun ESI–LC–MS/MS. The database's principles and structure are described. The database is called “2DE‐pattern” as it contains multiple isoform‐centric patterns of proteoforms separated according to 2DE principles. The database can be freely used at http://2de-pattern.pnpi.nrcki.ru.
Enzymes capable of modifying the sulfated polymeric molecule of fucoidan are mainly produced by different groups of marine organisms: invertebrates, bacteria, and also some fungi. We have discovered and identified a new strain of filamentous fungus Fusarium proliferatum LE1 (deposition number in Russian Collection of Agricultural Microorganisms is RCAM02409), which is a potential producer of fucoidan-degrading enzymes. The strain LE1 (RCAM02409) was identified on the basis of morphological characteristics and analysis of ITS sequences of ribosomal DNA. During submerged cultivation of F. proliferatum LE1 in the nutrient medium containing natural fucoidan sources (the mixture of brown algae Laminaria digitata and Fucus vesiculosus), enzymic activities of α-L-fucosidase and arylsulfatase were inducible. These enzymes hydrolyzed model substrates, para-nitrophenyl α-L-fucopyranoside and para-nitrophenyl sulfate, respectively. However, the α-L-fucosidase is appeared to be a secreted enzyme while the arylsulfatase was an intracellular one. No detectable fucoidanase activity was found during F. proliferatum LE1 growth in submerged culture or in a static one. Comparative screening for fucoidanase/arylsulfatase/α-L-fucosidase activities among several related Fusarium strains showed a uniqueness of F. proliferatum LE1 to produce arylsulfatase and α-L-fucosidase enzymes. Apart them, the strain was shown to produce other glycoside hydrolyses.
The use of tumor markers aids in the early detection of cancer recurrence and prognosis. There is a hope that they might also be useful in screening tests for the early detection of cancer. Here, the question of finding ideal tumor markers, which should be sensitive, specific, and reliable, is an acute issue. Human plasma is one of the most popular samples as it is commonly collected in the clinic and provides noninvasive, rapid analysis for any type of disease including cancer. Many efforts have been applied in searching for “ideal” tumor markers, digging very deep into plasma proteomes. The situation in this area can be improved in two ways—by attempting to find an ideal single tumor marker or by generating panels of different markers. In both cases, proteomics certainly plays a major role. There is a line of evidence that the most abundant, so-called “classical plasma proteins”, may be used to generate a tumor biomarker profile. To be comprehensive these profiles should have information not only about protein levels but also proteoform distribution for each protein. Initially, the profile of these proteins in norm should be generated. In our work, we collected bibliographic information about the connection of cancers with levels of “classical plasma proteins”. Additionally, we presented the proteoform profiles (2DE patterns) of these proteins in norm generated by two-dimensional electrophoresis with mass spectrometry and immunodetection. As a next step, similar profiles representing protein perturbations in plasma produced in the case of different cancers will be generated. Additionally, based on this information, different test systems can be developed.
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