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PubMed2005

Analysis of phosphatase and tensin homolog tumor suppressor interacting proteins by in vitro and in silico proteomics.

The phosphatase and tensin homolog (PTEN) tumor suppressor is a multifunctional protein deregulated in many types of cancer. To date, a comprehensive documentation of PTEN interacting proteins has not been performed. The goal of our study was to characterize the PTEN interactome using affinity pull-down and tandem mass spectrometry (MS/MS). Wild-type PTEN cDNA was inserted into pTRC-His2 vector to create a 6-His tagged protein, which was expressed in Escherichia coli. Lysate from a human lymphoma cell line was used in pull-down assays, utilizing affinity for nickel-agarose beads. Bound proteins were eluted with imidazole, digested and analyzed on an LCQ DecaXP ion trap mass spectrometer. The nickel affinity pull-down efficiency was evaluated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis and Western blot analysis. Acquired data were searched against the NCBI nr.fasta nonredundant protein database using the SEQUEST algorithm and screened using INTERACT and ProteinProphet. All experiments were performed in duplicate with 6-His-lacZ serving as control. A total of 79 proteins were identified in the wild-type 6-His-PTEN pull-down by MS/MS. We further validated a subset of the proteins present in the PTEN interactome by performing immunoprecipitation using an anti-PTEN antibody and establishing the presence of the proteins in the immunocomplex by Western blot analysis. A search of published PTEN interactions was also performed using Online Mendelian Inheritance in Man, Human Protein Reference Database, the IntAct Project database, and PubMed. This in silico analysis confirmed 42 out of 79 (53%) of the proteins identified by MS/MS. The remaining 37 proteins represent probable PTEN interactions not previously documented in public databases or reported in the literature. These results highlight the value of combining both in vitro biochemical approaches with in silico analyses for a comprehensive study of protein-protein interactions.

Algorithms↗
PubMed2004

Comparative study of proteome between primary cancer and hepatic metastatic tumor in colorectal cancer.

AIM: To identify the differential proteins associated with colorectal cancer genesis and hepatic metastasis. METHODS: Hydrophobic protein samples were extracted from normal colorectal mucosa, primary cancer lesion and hepatic metastatic foci of colorectal cancer. With two-dimensional electrophoresis and image analysis, differentially expressed protein spots were detected, and the proteins were identified by matrix assisted laser desorption/ionization-time of flight-mass spectrometry and peptide mass fingerprint analysis. RESULTS: Significant alterations of the proteins in number and expression levels were discovered in primary cancer and hepatic metastatic foci, the expression of a number of proteins was lost in 25-40 ku, but protein spots was increased in 14-21 ku, compared with normal mucosa. Nine differentially expressed protein spots were identified. Three proteins expressed in normal mucosa, but lost in primary cancer and hepatic metastasis, were recognized as calmodulin, ribonuclease 6 precursor and mannosidase-alpha. Proapolipoprotein was expressed progressively from normal mucosa to primary cancer and hepatic metastasis. The differentially expressed protein of beta-globin was found in normal mucosa and hepatic metastatic tumor, but lost in primary cancer lesion. Cdc 42, a GTP-binding protein, was identified in hepatic metastasis. The protein spots of C4 from primary cancer, M7 and M9 from hepatic metastasis had less homology with the proteins in database. CONCLUSION: Variations of hydrophobic protein expression in colorectal cancer initiation and hepatic metastasis are significant and can be observed with two-dimensional electrophoresis. The expression of calmodulin, ribonuclease 6 precursor and mannosidase-alpha is lost but the expression of proapolipoprotein is enhanced which is associated with colorectal cancer genesis and hepatic metastasis. Cdc 42 and beta-globin are expressed abnormally in hepatic metastasis. Protein C4, M7 and M9 may be associated with colorectal cancer genesis and hepatic metastasis.

Adult↗
PubMed2002

Immunoproteomics of Helicobacter pylori infection and relation to gastric disease.

The Gram negative bacterium Helicobacter pylori is a human pathogen which infects the gastric mucosa and causes an inflammatory process leading to gastritis, ulceration and cancer. A systematic, proteome based approach was chosen to detect candidate antigens of H. pylori for diagnosis, therapy and vaccine development and to investigate potential associations between specific immune responses and manifestations of disease. Sera from patients with active H. pylori infection (n = 24), a control group with unrelated gastric disorders (n = 12) and from patients with gastric cancer (n = 6) were collected and analyzed for the reactivity against proteins of the strain HP 26695 separated by two-dimensional electrophoresis. Overall, 310 antigenic protein species were recognized by H. pylori positive sera representing about 17% of all spots separated. Out of the 32 antigens most frequently recognized by H. pylori positive sera, nine were newly identified and 23 were confirmed from other studies. Three newly identified antigens which belong to the 150 most abundant protein species of H. pylori, were specifically recognized by H. pylori positive sera: the predicted coding region HP0231, serine protease HtrA (HP1019) and Cag3 (HP0522). Other antigens were recognized differently by sera from gastritis and ulcer patients, which may identify them as candidate indicators for clinical manifestations. The data from these immunoproteomic analyses are added to our public database (http://www.mpiib-berlin.mpg.de/2D-PAGE). This platform enables one to compile many protein profiles and to integrate data from other studies, an approach which will greatly assist the search for more immunogenic proteins for diagnostic assays and vaccine design.

Antibodies, Bacterial↗
PubMed2006

Analysis of the cGMP/cAMP interactome using a chemical proteomics approach in mammalian heart tissue validates sphingosine kinase type 1-interacting protein as a genuine and highly abundant AKAP.

The cyclic nucleotide monophosphates cAMP and cGMP play an essential role in many signaling pathways. To analyze which proteins do interact with these second messenger molecules, we developed a chemical proteomics approach using cAMP and cGMP immobilized onto agarose beads, via flexible linkers in the 2- and 8-position of the nucleotide. Optimization of the affinity pull-down procedures in lysates of HEK293 cells revealed that a large variety of proteins could be pulled down specifically. Identification of these proteins by mass spectrometry showed that many of these proteins were indeed genuine cAMP or cGMP binding proteins. However, additionally many of the pulled-down proteins were more abundant AMP/ADP/ATP, GMP/GDP/GTP, or general DNA/RNA binding proteins. Therefore, a sequential elution protocol was developed, eluting proteins from the beads using solutions containing ADP, GDP, cGMP, and/or cAMP, respectively. Using this protocol, we were able to sequentially and selectively elute ADP, GDP, and DNA binding proteins. The fraction left on the beads was further enriched, for cAMP/cGMP binding proteins. Transferring this protocol to the analysis of the cGMP/cAMP "interactome" in rat heart ventricular tissue enabled the specific pull-down of known cAMP/cGMP binding proteins such as cAMP and cGMP dependent protein kinases PKA and PKG, several phosphodiesterases and 6 AKAPs, that interact with PKA. Among the latter class of proteins was the highly abundant sphingosine kinase type1-interating protein (SKIP), recently proposed to be a potential AKAP. Further bioinformatics analysis endorses that SKIP is indeed a genuine PKA interacting protein, which is highly abundant in heart ventricular tissue.

Adaptor Proteins, Signal Transducing↗
PubMed2006

Proteome analysis of multidrug resistance in vincristine-resistant human gastric cancer cell line SGC7901/VCR.

In order to elucidate the mechanisms of multidrug resistance (MDR) of vincristine-resistant human gastric carcinoma cell line SGC7901/VCR, 2-DE was used to separate the total proteins of SGC7901/VCR and its parental cell line SGC7901. PDQuest software was applied to analyze 2-DE images, and the differential protein spots were identified by both MALDI-TOF-MS and ESI-Q-TOF-MS. Then the differential expressional levels of partially identified proteins were determined by Western blot analysis and real-time RT-PCR. Furthermore, the association of heat shock protein (HSP27), one of the highly expressed proteins in sgc7901/vcr, with MDR was analyzed using antisense inhibition of HSP27. In this study, the well-resolved, reproducible 2-DE patterns of SGC7901/VCR and SGC7901 were established, and yielded about 1100 protein-spots each. All the 24 differential proteins between the two cell lines were identified, and the differential expression levels of the partial proteins were confirmed. The suppression of HSP27 expression by HSP27 antisense oligonucleotides could enhance vincristine chemosensitivity in sgc7901/vcr and induce the cells to exhibit apoptotic morphological features after vincristine treatment. The differentially expressed proteins could be divided into six groups based on their functions: calcium-binding proteins, chaperones, proteins involved in drug detoxification or repair of DNA damage, metabolic enzymes, proteins related to cellular structure, and proteins relative to signal transduction, some of which may contribute to MDR of human gastric carcinoma cell line SGC7901/VCR. These data will be valuable for further study of the mechanisms of MDR in human gastric cancer.

Amino Acid Sequence↗
PubMed2006

Combining functional and topological properties to identify core modules in protein interaction networks.

Advances in large-scale technologies in proteomics, such as yeast two-hybrid screening and mass spectrometry, have made it possible to generate large Protein Interaction Networks (PINs). Recent methods for identifying dense sub-graphs in such networks have been based solely on graph theoretic properties. Therefore, there is a need for an approach that will allow us to combine domain-specific knowledge with topological properties to generate functionally relevant sub-graphs from large networks. This article describes two alternative network measures for analysis of PINs, which combine functional information with topological properties of the networks. These measures, called weighted clustering coefficient and weighted average nearest-neighbors degree, use weights representing the strengths of interactions between the proteins, calculated according to their semantic similarity, which is based on the Gene Ontology terms of the proteins. We perform a global analysis of the yeast PIN by systematically comparing the weighted measures with their topological counterparts. To show the usefulness of the weighted measures, we develop an algorithm for identification of functional modules, called SWEMODE (Semantic WEights for MODule Elucidation), that identifies dense sub-graphs containing functionally similar proteins. The proposed method is based on the ranking of nodes, i.e., proteins, according to their weighted neighborhood cohesiveness. The highest ranked nodes are considered as seeds for candidate modules. The algorithm then iterates through the neighborhood of each seed protein, to identify densely connected proteins with high functional similarity, according to the chosen parameters. Using a yeast two-hybrid data set of experimentally determined protein-protein interactions, we demonstrate that SWEMODE is able to identify dense clusters containing proteins that are functionally similar. Many of the identified modules correspond to known complexes or subunits of these complexes.

Algorithms↗
PubMed2004

Information assessment on predicting protein-protein interactions.

BACKGROUND: Identifying protein-protein interactions is fundamental for understanding the molecular machinery of the cell. Proteome-wide studies of protein-protein interactions are of significant value, but the high-throughput experimental technologies suffer from high rates of both false positive and false negative predictions. In addition to high-throughput experimental data, many diverse types of genomic data can help predict protein-protein interactions, such as mRNA expression, localization, essentiality, and functional annotation. Evaluations of the information contributions from different evidences help to establish more parsimonious models with comparable or better prediction accuracy, and to obtain biological insights of the relationships between protein-protein interactions and other genomic information. RESULTS: Our assessment is based on the genomic features used in a Bayesian network approach to predict protein-protein interactions genome-wide in yeast. In the special case, when one does not have any missing information about any of the features, our analysis shows that there is a larger information contribution from the functional-classification than from expression correlations or essentiality. We also show that in this case alternative models, such as logistic regression and random forest, may be more effective than Bayesian networks for predicting interactions. CONCLUSIONS: In the restricted problem posed by the complete-information subset, we identified that the MIPS and Gene Ontology (GO) functional similarity datasets as the dominating information contributors for predicting the protein-protein interactions under the framework proposed by Jansen et al. Random forests based on the MIPS and GO information alone can give highly accurate classifications. In this particular subset of complete information, adding other genomic data does little for improving predictions. We also found that the data discretizations used in the Bayesian methods decreased classification performance.

Artificial Intelligence↗
PubMed2004

Cerebrospinal fluid protein patterns in neurodegenerative disease revealed by liquid chromatography-Fourier transform ion cyclotron resonance mass spectrometry.

This study demonstrates the power of a novel proteomic approach developed for the detection and identification of biological markers in body fluids. The goal was to observe alterations in the protein patterns of cerebrospinal fluid (CSF) related to amyotrophic lateral sclerosis (ALS), a neurodegenerative disorder with unknown etiology. In the experiments, tryptic digests of CSF from patients and healthy controls were analyzed by on-line capillary liquid chromatography-Fourier transform ion cyclotron resonance mass spectrometry. (FT-ICR MS) Typically, around 4000 peptides were detected in one such experiment, and a pattern recognition program was constructed for the data analysis to distinguish mass chromatograms from patients and controls. This strategy was evaluated comparing the peptide patterns of CSF spiked in vitro with a biomarker, with control CSF. The patterns were clearly separated and the tryptic peptides of the biomarker were successfully selected as characteristic peaks. Hence, the method was applied to compare mass chromatograms of CSF from 12 ALS-patients and 10 matched healthy controls. While no biomarker alone could be identified from the characteristic peaks, we were able to assign 4 out of 5 unknown samples correctly (i.e., 80% correctly diagnosed, 20% false-negative), and it would be 100% if we reject a possible outlier believed to be caused by an occlusion in the spinal CSF compartment. These findings are very promising, although the clinical relevance is not fully established due to the low number of unknown samples analyzed. In addition to the diagnostic potential, these results may be important steps towards understanding the neurodegenerative process.

Adult↗
PubMed2001

High-throughput proteomics using high-efficiency multiple-capillary liquid chromatography with on-line high-performance ESI FTICR mass spectrometry.

We report on the design and application of a high-efficiency multiple-capillary liquid chromatography (LC) system for high-throughput proteome analysis. The multiple-capillary LC system using commercial LC pumps was operated at a pressure of 10,000 psi to deliver mobile phases through a novel passive feedback valve arrangement that permitted mobile-phase flow path switching and efficient sample introduction. The multiple-capillary LC system uses several serially connected dual-capillary column devices. The dual-capillary column approach eliminates the time delays for column regeneration (or equilibration) since one capillary column was used for a separation while the other was being washed. Several serially connected dual-capillary columns and electrospray ionization (ESI) sources were operated independently and can be used either for "backup" operation or for parallel operation with other mass spectrometers. This high-efficiency multiple-capillary LC system utilizes switching valves for all operations, enabling automated operation. The separation efficiency of the dual-capillary column arrangement, optimal capillary dimensions (column length and packed particle size), capillary regeneration conditions, and mobile-phase compositions and their compatibility with electrospray ionization were investigated. A high magnetic field (11.4 T) Fourier transform ion cyclotron resonance (FTICR) mass spectrometer was coupled on-line with this high-efficiency multiple-capillary LC system using an ESI interface. The capillary LC provided a peak capacity of approximately 650, and the 2-D capillary LC-FTICR analysis provided a combined resolving power of > 6 x 10(7) components. For yeast cytosolic tryptic digests > 100,000 polypeptides were detected, and approximately 1,000 proteins could be characterized from a single capillary LC-FTICR analysis using the high mass measurement accuracy (approximately 1 ppm) of FTICR, and likely more if LC retention time information were also exploited for peptide identification.

Chromatography, Liquid↗
PubMed2004

Detection and identification of tumor-associated protein variants in human hepatocellular carcinomas.

The proteomic approach is a valuable tool to detect and identify proteins that are associated with cancer. In previous investigations on experimentally induced rat hepatomas, we detected aldose reductase-like protein (ARLP) as a highly significant marker protein. Our present study was intended to look for the presence of similar tumor-associated marker proteins on human hepatocellular carcinomas (HCC). We found several novel tumor-associated protein variants that represent members of the aldo-keto reductase (AKR) superfamily. Human aldose reductase-like protein-1 (hARLP-1) was the most prominent tumor-associated AKR member detected in HCC by 2-dimensional electrophoresis (2-DE) and identified by mass spectrometric fingerprinting. The enzyme was found in 4 distinct forms (hARLP-1, 36/7.4 (kd/pI); hARLP-2, 36/7.2; hARLP-3, 36/6.4; and hARLP-4, 33/7.35). In addition, a human aldose reductase-like protein (hARLP-5, 36/7.6) was identified that differed from hARLP-1 by 1 amino acid (D313N), indicating 2 allelic forms of the human aldose reductase-like gene. A novel antibody directed against common parts of the hARLPs revealed hARLP reactivity in human HCC by immunohistochemistry. Furthermore, aldose reductase (AR) was identified and characterized as a tumor-associated variant. In conclusion, in all investigated human HCCs at least one of the various types of the described tumor-associated proteins of the AKR superfamily was clearly present. Of these HCC samples, 95% were positive for hARLPs as proven by 2-DE analysis and/or by use of the antibody directed against hARLP. Thus, hARLP is a strong candidate for use as an immunohistochemical diagnostic marker of human HCC.

Adult↗
PubMed2006

Metabolite coupling in genome-scale metabolic networks.

BACKGROUND: Biochemically detailed stoichiometric matrices have now been reconstructed for various bacteria, yeast, and for the human cardiac mitochondrion based on genomic and proteomic data. These networks have been manually curated based on legacy data and elementally and charge balanced. Comparative analysis of these well curated networks is now possible. Pairs of metabolites often appear together in several network reactions, linking them topologically. This co-occurrence of pairs of metabolites in metabolic reactions is termed herein "metabolite coupling." These metabolite pairs can be directly computed from the stoichiometric matrix, S. Metabolite coupling is derived from the matrix ŝŝT, whose off-diagonal elements indicate the number of reactions in which any two metabolites participate together, where ŝ is the binary form of S. RESULTS: Metabolite coupling in the studied networks was found to be dominated by a relatively small group of highly interacting pairs of metabolites. As would be expected, metabolites with high individual metabolite connectivity also tended to be those with the highest metabolite coupling, as the most connected metabolites couple more often. For metabolite pairs that are not highly coupled, we show that the number of reactions a pair of metabolites shares across a metabolic network closely approximates a line on a log-log scale. We also show that the preferential coupling of two metabolites with each other is spread across the spectrum of metabolites and is not unique to the most connected metabolites. We provide a measure for determining which metabolite pairs couple more often than would be expected based on their individual connectivity in the network and show that these metabolites often derive their principal biological functions from existing in pairs. Thus, analysis of metabolite coupling provides information beyond that which is found from studying the individual connectivity of individual metabolites. CONCLUSION: The coupling of metabolites is an important topological property of metabolic networks. By computing coupling quantitatively for the first time in genome-scale metabolic networks, we provide insight into the basic structure of these networks.

Chromosome Mapping↗
PubMed2026

Spatial proteomics reveals four-stage molecular evolution in cancer immunotherapy-related gastritis.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have transformed cancer treatment, yet immune-related adverse events (irAEs) including immunotherapy-related gastritis (IRAEG) pose significant clinical challenges-often necessitating treatment interruption that may compromise antitumor efficacy. IRAEG presents with atypical symptoms, lacks specific biomarkers, and shows histopathological overlap with other forms of gastritis, complicating diagnosis and management. Despite increasing clinical recognition, a systematic understanding of spatial molecular alterations across the full disease course remains limited. Here, we used spatial proteomics to map the molecular landscape of IRAEG during disease progression and to define stage-specific patterns of molecular evolution relevant to cancer immunotherapy management. METHODS: We analyzed tissue samples from seven patients, including four non-immunotherapy-related gastritis controls and three cancer patients who developed IRAEG following ICI therapy for solid tumors, sampled longitudinally across four disease stages: baseline (G1), acute severe inflammation (G2), early recovery (G3), and complete recovery (G4). Using laser capture microdissection coupled with data-independent acquisition mass spectrometry, we profiled 177 spatially resolved gastric tissue regions. Multiplex immunohistochemistry and immunofluorescence characterized features of the immune microenvironment, while Gene Ontology, KEGG pathway analysis, Gene Set Variation Analysis, and xCell inference enabled functional, metabolic, and immune profiling. Key immune and NET-related findings were further validated by multiplex immunofluorescence in an independent, expanded cohort of IRAEG and non-immunotherapy-related gastritis samples. RESULTS: IRAEG was characterized by widespread HLA molecule activation and enhanced antigen processing, resembling the immune phenotype observed in organ transplant rejection. The acute G2 stage exhibited excessive neutrophil extracellular trap formation, profound metabolic suppression, and collapse of immune homeostasis-features that may inform early intervention strategies to preserve ICI treatment continuity. During early recovery (G3), inflammatory injury transitioned toward repair, marked by activation of fatty acid metabolism and PPAR signaling. Notably, even at complete clinical recovery (G4), more than 1,000 proteins remained differentially expressed, reflecting sustained enhancement of metabolic and immune functions and establishing a distinct molecular "memory" state with implications for ICI rechallenge decisions. CONCLUSIONS: These findings define four molecularly distinct stages of IRAEG progression and recovery. The stage-specific signatures identified here serve as candidate biomarkers for diagnosis, disease staging, and therapeutic response assessment, and may guide clinical decisions regarding irAE management, treatment modification, and safe ICI rechallenge to support continued antitumor therapy.

Humans↗
PubMed2013

The identification of novel potential injury mechanisms and candidate biomarkers in renal allograft rejection by quantitative proteomics.

Early transplant dysfunction and failure because of immunological and nonimmunological factors still presents a significant clinical problem for transplant recipients. A critical unmet need is the noninvasive detection and prediction of immune injury such that acute injury can be reversed by proactive immunosuppression titration. In this study, we used iTRAQ -based proteomic discovery and targeted ELISA validation to discover and validate candidate urine protein biomarkers from 262 renal allograft recipients with biopsy-confirmed allograft injury. Urine samples were randomly split into a training set of 108 patients and an independent validation set of 154 patients, which comprised the clinical biopsy-confirmed phenotypes of acute rejection (AR) (n = 74), stable graft (STA) (n = 74), chronic allograft injury (CAI) (n = 58), BK virus nephritis (BKVN) (n = 38), nephrotic syndrome (NS) (n = 8), and healthy, normal control (HC) (n = 10). A total of 389 proteins were measured that displayed differential abundances across urine specimens of the injury types (p < 0.05) with a significant finding that SUMO2 (small ubiquitin-related modifier 2) was identified as a "hub" protein for graft injury irrespective of causation. Sixty-nine urine proteins had differences in abundance (p < 0.01) in AR compared with stable graft, of which 12 proteins were up-regulated in AR with a mean fold increase of 2.8. Nine urine proteins were highly specific for AR because of their significant differences (p < 0.01; fold increase >1.5) from all other transplant categories (HLA class II protein HLA-DRB1, KRT14, HIST1H4B, FGG, ACTB, FGB, FGA, KRT7, DPP4). Increased levels of three of these proteins, fibrinogen beta (FGB; p = 0.04), fibrinogen gamma (FGG; p = 0.03), and HLA DRB1 (p = 0.003) were validated by ELISA in AR using an independent sample set. The fibrinogen proteins further segregated AR from BK virus nephritis (FGB p = 0.03, FGG p = 0.02), a finding that supports the utility of monitoring these urinary proteins for the specific and sensitive noninvasive diagnosis of acute renal allograft rejection.

Acute Kidney Injury↗
PubMed2006

A method for automatically interpreting mass spectra of 18O-labeled isotopic clusters.

16O/18O labeling is one differential proteomics technology among many that promises diagnostic and prognostic biomarkers of disease. Although the incorporation of 18O in the C-terminal carboxyl group during endoproteinase digestion in the presence of H2 18O makes the process of labeling facile, the ease and effectiveness of label incorporation have in some regards been outweighed by the difficulties in interpreting the resulting spectra. Complex isotope patterns result from the composition of unlabeled (18O(0)), singly labeled (18O(1)), and doubly labeled species (18O(2)) as well as contributions from the naturally occurring isotopes (e.g. 13C and 15N). Moreover because labeling is enzymatic, the number of 18O atoms incorporated can vary from peptide to peptide. Finally it is difficult to distinguish highly up-regulated from highly down-regulated or C-terminal peptides. We have developed an algorithm entitled regression analysis applied to mass spectrometry (RAAMS) that automatically, rapidly, and confidently interprets spectra of 18O-labeled peptides without requiring chemical composition information derived from product ion spectra. The algorithm is able to measure the effective 18O incorporation rate due to variable enzyme substrate specificity of the pseudosubstrate during the isotope exchange reaction and corrects for the 18O(0) abundance that remains in the labeled sample when using a two-step digestion/labeling procedure. We have also incorporated a method for distinguishing pure 18O(0) from pure 18O(2) peptides utilizing impure H2 18O. The algorithm operates on centroided peak lists and is therefore very fast: nine chromatograms of, on average, 1,168 spectra and containing, on average, 6,761 isotopic clusters were interpreted in, on average, 45 s per chromatogram. RAAMS is fast enough (average, 38 ms/spectrum) to allow the possibility of performing information-dependent MS/MS on a chromatographic time scale on species exceeding predetermined ratio thresholds. We describe in detail the operation of the algorithm and demonstrate its use on datasets with known and unknown ratios.

Algorithms↗
PubMed2002

Primer on medical genomics. Part IV: Expression proteomics.

Proteomics, simply defined, is the study of proteomes. More completely, proteomics is defined as the study of all proteins, including their relative abundance, distribution, posttranslational modifications, functions, and interactions with other macromolecules, in a given cell or organism within a given environment and at a specific stage in the cell cycle. Proteins carry out the biological functions encoded by genes; hence, once the initial stage of genome sequencing and gene discovery is completed, a study of the proteome must be undertaken to address fundamental biological questions. The 3 broad areas are expression proteomics, which catalogues the relative abundance of proteins; cell-mapping or cellular proteomics, which delineates functional protein-protein interactions and organelle-specific protein distribution; and structural proteomics, which characterizes the 3-dimensional structure of proteins. With these approaches, proteins are studied on a global scale using a synergistic combination of powerful, high-throughput technologies, including 2-dimensional polyacrylamide gel electrophoresis, mass spectrometry, multidimensional liquid chromatography, and bioinformatics. Mass spectrometry, which provides highly accurate molecular mass measurements, has emerged as the analytical technology of choice for protein identification, characterization, and sequencing. This task has been made considerably easier with the availability of complete, nonredundant, and annotated genome sequence databases for many organisms. This article reviews the area of expression proteomics.

Biotechnology↗
PubMed2004

[2-DE profiling and differential analysis of human bronchial epithelial tissues in different stages of carcinogenesis].

BACKGROUND & OBJECTIVE: The carcinogenesis of bronchial epithelial cells is a complex multiple-stage process involving multiple genes, but its mechanism remains unclear. Studying this process with proteomic approaches may identify carcinogenesis-associated proteins, which are important for elucidating carcinogenic mechanism of human lung squamous carcinoma. This study was designed to optimize the protein preparation methods for bronchial epithelial tissues, to establish two-dimensional gel electrophoresis profiles of human bronchial epithelial tissues from different stages in carcinogenic process, and to perform differential analysis and provide a basis for identifying carcinogenesis-associated proteins of lung squamous carcinoma. METHODS: After obtaining samples of the normal, metaplasia, dysplasia, carcinoma tissues of human bronchial epithelia, modified deoxycholate- trichloroaetic acid (DOC-TCA) precipitation was used to extract and purify the total proteins of bronchial epithelial samples. Immobilized pH gradient two-dimensional polyacrylamide gel electrophoresis (2-DE) was used to separate the total proteins of the samples. After silver staining, ImageMaster 2-DE image analysis software was applied to analyze 2-DE images. Some selected differential protein spots were identified by peptide mass fingerprint (PMF) based on matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) and database search. RESULTS: The total proteins extracted with the method described here were used to perform 2-DE. 2-DE patterns with high resolution and reproducibility from different stages were obtained. The average spots for normal epithelium, metaplasia, dysplasia and invasive carcinoma were 1189.50+/-39.89, 1227.00+/-37.90, 1273.00+/-43.31, and 1326.00+/-66.63, respectively. The test was repeated, which showed that there were average 1216 +/- 75 spots among 3 gels of the same metaplasia tissues and 1082 +/- 67 spots were matched. The average matching rate was 89.3% and protein spots in 3 gels had good reproducibility. The average position deviation of matched spots in different gels was 0.865+/- 0.247 mm in IEF direction, and 0.971+/- 0.104 mm in SDS-PAGE direction. The 2D images of 32 samples were compared, which showed a significant difference of the number of average protein spots among the four groups (P< 0.05). The average differential spots between the low and advanced stages were 31.50 +/- 7.67, 41.00 +/- 9.07, and 56.00 +/- 8.96, respectively. Twenty-three protein spots chosen randomly from dysplasia and invasive carcinoma groups were identified by PMF, some of which were involved in the cell proliferation, differentiation, cycle regulation, signal transduction and tumor occurrence. CONCLUSIONS: (1) Improved DOC-TCA precipitation is a preferable method for protein preparation from bronchial epithelial tissues. (2) We established well-resolved, reproducible 2-DE profiles of human bronchial epithelial tissue in different stages of carcinogenesis. Some differentially expressed-proteins may be related to carcinogenesis of bronchial epithelia. These results provide a fundamental basis for further study of carcinogenic mechanism of lung squamous cancer and screening its specific marker.

Bronchi↗
PubMed2003

A reference map of a human pituitary adenoma proteome.

In order to compare the proteomes from different cell types of pituitary adenomas for our long-term goal to clarify the molecular mechanisms that participate in the formation of pituitary adenoma, and to detect any tumor-related marker for an "early-stage" diagnosis, the two-dimensional gel electrophoresis (2-DE) reference map of a pituitary adenoma tissue proteome is described here. A vertical, two-dimensional (2-D) polyacrylamide gel electrophoresis system and PDQuest image analysis software have been used to provide a high level of between-gel reproducibility and to accurately array each protein expressed in a pituitary adenoma tissue. Mass spectrometry (matrix-assisted laser desorption/ionization-time of flight MALDI-TOF and liquid chromatography-electrospray ionization-quadrupole-ion trap LC-ESI-Q-IT) and protein databases were used to characterize each protein in the 2-D gel. The results demonstrate that a good reproducibility of the 2-D gel pattern was attained. The position deviation of matched spots among four 2-D gels was 1.95 +/- 0.45 mm in the isoelectric focusing direction, and 1.70 +/- 0.53 mm in the sodium dodecyl sulfate-polyacrylamide gel electrophoresis direction. A total of ca. 1000 protein spots were separated by 2-DE, and 135 protein spots that represent 111 proteins were characterized with mass spectrometry (96 spots for MALDI-TOF, 39 spots for LC-ESI-Q-IT). The characterized proteins include pituitary hormones, cellular signals, enzymes, cellular-defense proteins, cell-structure proteins, transport proteins, etc. Those proteins were located in the cytoplasmic, cellular membrane, mitochondrial, endoplasmic reticulum, nuclear, ribonucleosome, extracellular fractions, or were secreted in plasma, etc. Those identified proteins contribute to a functional profile of the pituitary adenoma proteome. These data will be used to expand the proteome database of the human pituitary, which can be accessed in the website http://www.utmem.edu /proteomics.

Adenoma↗
PubMed2000

A two dimensional electrophoresis database of a human Jurkat T-cell line.

About 2000 protein spots of human Jurkat T-cells were detected by high resolution two-dimensional gel electrophoresis (2-DE) and were characterized in terms of their isoelectric point and molecular mass. A 2-DE database was constructed and is available at http://www.mpiib-berlin.mpg.de/2D-PAGE/. At present the database contains 67 identified protein spots. These proteins were identified after tryptic digestion by peptide mass fingerprinting with delayed extraction-matrix assisted laser desorption/ionization-mass spectrometry (DE-MALDI-MS). Proteins with a sequence coverage of at least 30% were introduced in the database. This sequence coverage could not always be obtained by using only the matrix alpha-cyano-4-hydroxycinnamic acid (CHCA) for the mass analysis. Therefore, an additional mass spectrum was recorded by using 2,5-dihydroxybenzoic acid (DHB). Usually, additional mass peaks were detected and together with the mass spectrum of CHCA this resulted in the desired sequence coverage.

Coloring Agents↗