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PubMed2006

Tissue heterogeneity of the mammalian mitochondrial proteome.

The functionality of the mitochondrion is primarily determined by nuclear encoded proteins. The mitochondrial functional requirements of different tissues vary from a significant biosynthetic role (liver) to a primarily energy metabolism-oriented organelle (heart). The purpose of this study was to compare the mitochondrial proteome from four different tissues of the rat, brain, liver, heart, and kidney, to provide insight into the extent of mitochondrial heterogeneity and to further characterize the overall mitochondrial proteome. Mitochondria were isolated, solubilized, digested, and subjected to quantitative liquid chromatography-mass spectroscopy. Of the 16,950 distinct peptides detected, 8,045 proteins were identified. High-confidence identification threshold was reached by 1,162 peptides, which were further analyzed. Of these 1,162 proteins, 1,149 were significantly different in content (P and q values < 0.05) between at least 2 tissues, whereas 13 were not significantly different between any tissues. Confirmation of the mitochondrial origin of proteins was determined from the literature or via NH(2)-terminal mitochondrial localization signals. With these criteria, 382 proteins in the significantly different groups were confirmed to be mitochondrial, and 493 could not be confirmed to be mitochondrial but were not definitively localized elsewhere in the cell. A total of 145 proteins were assigned to the rat mitochondrial proteome for the first time via their NH(2)-terminal mitochondrial localization signals. Among the proteins that were not significantly different between tissues, three were confirmed to be mitochondrial. Most notable of the significantly different proteins were histone family proteins and several structural proteins, including tubulin and intermediate filaments. The mitochondrial proteome from each tissue had very specific characteristics indicative of different functional emphasis. These data confirm the notion that mitochondria are tuned by the nucleus for specific functions in different tissues.

Amino Acid Sequence↗
PubMed2002

Activity-based probes for functional proteomics.

Achieving an understanding of the functional role of all proteins expressed by a complex organism will require the development of methods that enable the rapid monitoring of protein function on a global scale. Current genomics and proteomics technologies fall short of this goal since they measure only the relative abundance of transcripts and proteins, respectively. Recent efforts in several laboratories have led to the development of tagged chemical probes that selectively react with families of active enzymes based on shared mechanistic features. These activity-based probes (ABPs) permit the quantitation and comparison of multiple protein activities simultaneously in complex proteomes. In this paper, the general properties and design features of ABPs will be discussed with an emphasis on the use of ABPs for activity-based proteome analysis.

Affinity Labels↗
PubMed2003

Proteomic profiling of proteins associated with urokinase plasminogen activator receptor in a colon cancer cell line using an antisense approach.

Expression of urokinase plasminogen activator (uPA) and its receptor (uPAR) strongly correlates with a malignant tumour cell phenotype. In the multistep process of metastasis, uPA binding to uPAR influences different cellular functions. In the present study, a highly metastatic colon cancer cell line, HCT116 was transfected with an expression vector containing a 5' uPAR cDNA fragment in an antisense orientation. This construct was most effective in reducing uPAR cell surface expression as confirmed by flow cytometry analysis. Antisense transfection of HCT116 cells had no effect on proliferation but the following effects were observed: (1) a 1.3-fold decreased adhesion; (2) a two-fold decreased Erk MAP kinase activity; (3) a 2.7-fold decrease in Src kinase activity; (4) a 1.5- and two-fold decrease in uPA cell surface expression and secretion; (5) abrogation of promatrix metalloproteinase-9 secretion; and (6) a complete suppression of plasminogen-dependent matrix degradation. Using proteomic analysis, we demonstrate loss of approximately 200 proteins and quantitative differences in the expression of 141 other proteins in an antisense-clone compared to wild-type and mock-transfected control. Such changes in protein expression with the down-regulation of uPAR may be an important contributor in colon cancer progression and metastasis and may not only provide a basis to develop a proteomic data bank of uPAR-mediated signaling molecules but may also lead to the development of therapeutic approaches for the cure and better management of colon cancer.

5' Untranslated Regions↗
PubMedDate not supplied

UV-induced melanoma cell lines and their potential for proteome analysis: a review.

We have established cell lines from benign cutaneous melanocytic lesions and from melanoma-affected lymph nodes of monodelphis domestica, the laboratory opossum (a South American marsupial now widely maintained in captive colonies for experimental purposes). Unlike melanoma cell lines currently available from humans and other mammals, the opossum lines are derived from cells transformed in vivo by experimentally controlled exposure to ultraviolet B (UVB) radiation of known spectral composition. Differences in the patterns of protein expression among cell lines at different stages of the UVB-induced melanoma cascade can be identified by proteome analysis and will provide a useful basis for comparisons with human and mouse melanoma cell lines. Powerful new two-dimensional (2D) gel electrophoresis technologies and sophisticated bioinformatics programs make it possible to carry out qualitative and quantitative analyses of the entire protein complement expressed by the genome (proteome) of a specific cell type. One area of biology particularly well suited to proteome analysis is carcinogenesis. It is now feasible, for example, to attempt to characterize the full repertoire of proteins, including all the antigenic determinants at the cell surface and in the cytosol, during the carcinogenic cascade from normal progenitor cells, to benign tumor cells, and finally, to highly invasive metastatic cells. Proteome analyses have been initiated with the cell lines from M. domestica.

Animals↗
PubMed2006

Bioinformatics and data mining in proteomics.

Proteomic studies involve the identification as well as qualitative and quantitative comparison of proteins expressed under different conditions, and elucidation of their properties and functions, usually in a large-scale, high-throughput format. The high dimensionality of data generated from these studies will require the development of improved bioinformatics tools and data-mining approaches for efficient and accurate data analysis of biological specimens from healthy and diseased individuals. Mining large proteomics data sets provides a better understanding of the complexities between the normal and abnormal cell proteome of various biological systems, including environmental hazards, infectious agents (bioterrorism) and cancers. This review will shed light on recent developments in bioinformatics and data-mining approaches, and their limitations when applied to proteomics data sets, in order to strengthen the interdependence between proteomic technologies and bioinformatics tools.

Computational Biology↗
PubMed2002

Trends in mass spectrometry instrumentation for proteomics.

Mass spectrometry has become a primary tool for proteomics because of its capabilities for rapid and sensitive protein identification and quantitation. It is now possible to identify thousands of proteins from microgram sample quantities in a single day and to quantify relative protein abundances. However, the need for increased capabilities for proteome measurements is immense and is now driving both new strategies and instrument advances. These developments include those based on integration with multi-dimensional liquid separations and high accuracy mass measurements and promise more than order of magnitude improvements in sensitivity, dynamic range and throughput for proteomic analyses in the near future.

Biotechnology↗
PubMed2001

Proteome study of colorectal carcinogenesis.

Development of cancer is a complex process involving multiple changes in gene expression. To unravel these alterations, a proteome approach aimed at the identification of qualitative and quantitative changes in protein composition, including their post-translational modifications, attracts great attention. Our study was focused on the identification of proteins whose amount is altered in the course of malignant transformation of colon mucosa. Proteins extracted from tissue specimens or cell lysates were separated by two-dimensional gel electrophoresis (2-DE). Comparative analyses of 2-DE protein patterns were done using computerized image analysis. Selected proteins exhibiting statistically significant abundance alterations comparing healthy and diseased tissues were identified by mass spectrometry. Globally, we have found 57 proteins that exhibited either a significant decrease or increase in amount in pathological tissues, and 18 of these were annotated by mass spectrometry. The alterations in the expression of nine proteins were common for both precancerous and neoplastic tissues suggesting their role in colon tumorigenesis. The epithelial origin of all identified spots was checked in two cell lines Caco-2 and DLD-1 originating from well-differentiated and poorly differentiated colon carcinoma, respectively.

Adenocarcinoma↗
PubMed2002

Genomics and proteomics analysis of acetaminophen toxicity in mouse liver.

Overdose of acetaminophen (APAP) causes severe centrilobular hepatic necrosis in humans and experimental animals. Here, to explore its mechanism, we administered APAP at subtoxic (150 mg/kg ip) and toxic (500 mg/kg ip) doses to overnight fasted mice. Animals were sacrificed at different time points from 15 min to 4 h postinjection. We assessed liver toxicity by plasma ALT activity and by electron microscopy. Using nylon filter arrays and RTQPCR, we performed genomics analysis in liver. We ran proteomics on liver mitochondrial subfractions using the newly developed quantitative fluorescent 2D-DIGE method (Amersham Pharmacia Biotech UK Limited). As soon as 15 min postinjection, centrilobular hepatocyte mitochondria were already slightly enlarged and GSH total content dropped by a third at top dose. GM-CSF mRNA, which is a granulocyte specific gene likely coming from resident Kupffer cells, was also induced to its maximum of 3-fold at both doses. Chaperone proteins Hsp10 and Hsp60 were readily decreased by half in mitochondria at both doses, most likely by leaking into cytoplasm. Although APAP is known as an apoptotic trigger, no apoptosis was observed at any time point. Most of the protein changes in mitochondria were present at 15 min postinjection, thus preceding most of the gene regulations. The decrease of ATP synthase subunits and beta-oxidation pathway proteins indicated a loss of energy production. As the morphology of mitochondria was also affected very early at top dose, we concluded that APAP toxicity was a direct action of its known reactive metabolite NAPQI, rather than a consequence of gene regulation. However, the latter will either worsen the toxicity or lead toward cell recovery depending on the cellular damage level.

Acetaminophen↗
PubMed2025

Four-dimensional data independent acquisition proteomics and metabolomics reveal mechanisms of hydrogen-rich water at Zusanli (ST36) point against triple-negative breast cancer in mice.

OBJECTIVE: To develop a safe and effective green therapy for triple-negative breast cancer, this study combines hydrogen-rich water with acupuncture point injection, and finds that it can prevent tumor growth and minimize cancer metastasis. METHODS: After 21 d of hydrogen rich water injection treatment on 4T1 (mouse breast cancer cells) xenograft mice, in order to systematically identify differentially expressed proteins in tumor samples between the model group and the Zusanli (ST36) group injected with hydrogen rich water at acupoints, with a focus on functional proteins or signaling pathways related to tumor occurrence and development, researchers conducted four-dimensional data independent acquisition (4D-DIA) proteomic analysis on tumor tissues. In order to further investigate the dynamic changes of metabolites after therapeutic intervention, researchers conducted liquid chromatography-tandem mass spectrometry untargeted metabolomics identification and analysis on mouse serum. The results of the joint proteomics-metabolomics analysis were validated using experimental methods such as immunofluorescence, Western blotting, and quantitative reverse transcription polymerase chain reaction detection. RESULTS: Injecting hydrogen-rich water into acupoints significantly inhibited tumor growth (P < 0.05). 4D-DIA proteomics and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses uncovered pathways such as T helper 1 cell (Th1) and T helper 2 cell (Th2) cell differentiation. The KEGG metabolic pathways identified in the metabolomics analysis included galactose metabolism along with fructose and mannose metabolism. Based on the combined proteomics and metabolomics analysis, the key pathways included the C-type lectin receptor signaling pathway. The major cancer-related differential proteins detected in Th1 and Th2 cell differentiation [interleukin 6 signal transducer, nuclear factor of activated T cells 4, recombinant mitogen activated protein kinase 10 (MAPK10), and MAPK11] were upregulated after the injection of hydrogen-rich water into the Zusanli (ST36) acupoint, whereas Linker for activation of T cells (Lat), signal transducer and activator of transcription 1, and protein kinase C, theta were downregulated. CONCLUSION: The injection of hydrogen-rich water into the Zusanli (ST36) acupoint effectively inhibited the hyperplasia of 4T1 BC cells and enhanced their apoptosis, potentially exerting a therapeutic effect through multiple pathways and targeting various sites.

Animals↗
PubMedDate not supplied

Peptidomics: identification and quantification of endogenous peptides in neuroendocrine tissues.

Neuropeptides perform a large variety of functions as intercellular signaling molecules. While most proteomic studies involve digestion of the proteins with trypsin or other proteases, peptidomics studies usually analyze the native peptide forms. Neuropeptides can be studied by using mass spectrometry for identification and quantitation. In many cases, mass spectrometry provides an understanding of the precise molecular form of the native peptide, including post-translational cleavages and other modifications. Quantitative peptidomics studies generally use differential isotopic tags to label two sets of extracted peptides, as done with proteomic studies, except that the Cys-based reagents typically used for quantitation of proteins are not suitable because most peptides lack Cys residues. Instead, a number of amine-specific labels have been created and some of these are useful for peptide quantitation by mass spectrometry. In this review, peptidomics techniques are discussed along with the major findings of many recent studies and future directions for the field.

Animals↗
PubMed2004

Identifying biomarkers and surrogates of tumors (cancer biometrics): correlation with immunotherapies and immune cells.

The presence of inflammatory cells within cancer has been described for quite some time by pathologists, with generally improved outcome associated with their presence in various epithelial neoplasms. Most remarkably, this has included dendritic cells and T cells but more recently NK cells as well. Coupled with the rapid evolution of molecular technology, microarray analyses of primary tumors, serum and tumor proteomics, tumor capture analyses in the peripheral blood (together with quantitative RT-PCR), and novel histochemical markers and tissue microarrays, this provides the opportunity to establish a more effective means to study and classify into subsets various forms of cancer. Much of the current controversy in cancer diagnosis and pathologic assessment of prognosis lies in the application of these techniques in concert with other molecular tools including DNA microarrays, expression of histochemically defined cytokines, proangiogenic factors, and oncogene products, and correlating this with clinical relevance. Molecular detection technologies such as reverse transcriptase polymerase chain reaction, proteomics, and microarray analyses will be validated based on their integration with conventional cancer pathology and cancer diagnostics. Further work is needed to establish which cancer biomarkers and surrogates should be routinely measured and in which settings, and determining the appropriate sample size for such assays that can be validated in retrospective and prospective clinical studies. The ability to integrate these rapidly evolving strategies will consume much of our coordinate effort in cancer and cancer therapeutics for the near future.

Biomarkers, Tumor↗
PubMed2000

In vitro selection of nucleic acids for diagnostic applications.

In vitro selection methods have proven to be extraordinarily adept at generating a wide variety of nucleic acid-binding species (aptamers) and catalysts (ribozymes). To date, selected nucleic acids have primarily been of academic interest. However, just as antibodies have proven utility as 'universal receptors' that can be crafted against a huge variety of ligands and can be readily adapted to diagnostic assays, aptamers may yet find application in assays. A new class of research reagents, aptazymes, are not mere mimics of antibodies but in fact allow the direct transduction of molecular recognition to catalysis. Aptamers and aptazymes may prove to be uniquely useful for the development of chip arrays for the detection and quantitation of a wide range of molecules in organismal proteomes and metabolomes.

Animals↗
PubMed2026

iModMix: integrative module analysis for multi-omics data.

SUMMARY: Integrative Module Analysis for Multi-omics Data (iModMix) is a biology-agnostic framework that enables the discovery of novel associations across any type of quantitative abundance data, including but not limited to transcriptomics, proteomics, and metabolomics. Instead of relying on pathway annotations or prior biological knowledge, iModMix constructs data-driven modules using graphical lasso to estimate sparse networks from omics features. These modules are summarized into eigenfeatures and correlated across datasets for horizontal integration, while preserving the distinct feature sets and interpretability of each omics type. iModMix operates directly on matrices containing expression or abundances for a wide range of features, including but not limited to genes, proteins, and metabolites. Because it does not rely on annotations (e.g., KEGG identifiers), it can seamlessly incorporate both identified and unidentified metabolites, addressing a key limitation of many existing metabolomics tools. iModMix is available as a user-friendly R Shiny application requiring no programming expertise (https://imodmix.moffitt.org), and as a Bioconductor R package for advanced users (https://bioconductor.org/packages/release/bioc/html/iModMix.html). The tool includes several public and in-house datasets to illustrate its utility in identifying novel multi-omics relationships in diverse biological contexts. AVAILABILITY AND IMPLEMENTATION: iModMix is freely available from Bioconductor (https://bioconductor.org/packages/release/bioc/html/iModMix.html), and the example dataset package (iModMixData) is also available from Bioconductor (https://bioconductor.org/packages/release/ data/experiment/html/iModMixData.html). The R package source code and Docker are available from GitHub: https://github.com/biodatalab/iModMix. Shiny application can be accessed at: https://imodmix.moffitt.org.

Multiomics↗
PubMed2007

The PROTICdb database for 2-DE proteomics.

PROTICdb is a web-based database mainly designed to store and analyze plant proteome data obtained by 2D polyacrylamide gel electrophoresis (2D PAGE) and mass spectrometry (MS). The goals of PROTICdb are (1) to store, track, and query information related to proteomic experiments, i.e., from tissue sampling to protein identification and quantitative measurements; and (2) to integrate information from the user's own expertise and other sources into a knowledge base, used to support data interpretation (e.g., for the determination of allelic variants or products of posttranslational modifications). Data insertion into the relational database of PROTICdb is achieved either by uploading outputs from Mélanie, PDQuest, IM2d, ImageMaster(tm) 2D Platinum v5.0, Progenesis, Sequest, MS-Fit, and Mascot software, or by filling in web forms (experimental design and methods). 2D PAGE-annotated maps can be displayed, queried, and compared through the GelBrowser. Quantitative data can be easily exported in a tabulated format for statistical analyses with any third-party software. PROTICdb is based on the Oracle or the PostgreSQLDataBase Management System (DBMS) and is freely available upon request at http://cms.moulon.inra.fr/content/view/14/44/.

Databases, Protein↗
PubMed2026

Machine Learning in Hyperlipidaemia Research: Screening and Experimental Insights into Lipid Metabolism Modulators.

Hyperlipidemia, characterized by elevated blood lipid levels, represents a major global health concern due to its strong association with cardiovascular disease, diabetes, and metabolic syndrome. While current therapies - such as statins, fibrates, bile acid sequestrants, and PCSK9 inhibitors - are effective in controlling hyperlipidemia, they are often associated with adverse effects, potential drug resistance, and suboptimal efficacy in certain patient populations. All of the above underscore the urgent need for safer and more effective therapeutic alternatives. Among the major molecular targets involved in the regulation of lipid metabolism are HMG-CoA reductase, PCSK9, peroxisome proliferator-activated receptors (PPARs), cholesteryl ester transfer protein (CETP), and nuclear receptors, including the liver X receptor (LXR) and farnesoid X receptor (FXR), which are also targets for future antihyperlipidemic drug development. Recent advancements in artificial intelligence (AI) and machine learning (ML) have significantly transformed and accelerated drug discovery by enabling the processing of vast amounts of genomic, proteomic, and chemical data. Furthermore, ML tools such as quantitative structure-activity relationship (QSAR) modelling, deep learning, random forest, and support vector machines (SVM) have proven predictive and effective in identifying novel lipid metabolism modulators, thereby enhancing the efficacy and accuracy of virtual screening. Meanwhile, molecular docking has become an integral part of structure-based drug design (SBDD), and software such as AutoDock, Glide, and GOLD have proven effective in generating accurate ligand-target docking models. Molecular docking, together with ML-based approaches, enables the identification of potent and selective drug candidates. Overall, the combination of ML and molecular docking offers an efficient and accurate platform for antihyperlipidemic drug discovery, helping to overcome the limitations of currently available therapeutic strategies.

HMG-CoA reductase↗
PubMed2003

[DNA chips for gene expression analysis and their application in diagnostics].

Due to large genome projects more and more genetic information and more and more data about genetic background of various diseases can be obtained. By means of microarrays thousands to hundreds of thousands of molecular analysis can be performed at a time, rapidly, under identical conditions, hereby microarrays provide a solution to continuously increasing throughput demands from large genomic, pharmacogenetic and proteomic programmes. Microarrays offer unprecedented opportunities for qualitative and quantitative analysis of gene expression, understanding gene function and detection gene alterations, polimorphisms. Microarray expression profiles will define stages of different tumor types and new prognostic subgroups in cancer and other diseases. For individual patients, more precise diagnosis and risk assessment based on expression profiles are already achievable, leading to more accurate determination of prognosis and more individually tailored treatment. Microarrays will play an important role in diagnostics of infectious diseases and identification of novel diagnostic and therapeutic targets. Wide-spread application of microarrays in clinical practice is not typical today, but their production on an industrial scale and the increased information provided by microarrays should assure their entry into clinical practice as very powerful, sensitive, specific and rapid tools of diagnostics of various diseases.

Communicable Diseases↗
PubMed2004

Molecular diagnostics of malignant disorders.

Expansion of our understanding of the molecular basis of cancer has enabled us to apply molecular techniques to categorize malignancies into more uniform, informative groups. In this review we describe the basic molecular techniques used for this purpose, including Southern blotting, polymerase chain reaction (PCR) and quantitative PCR, fluorescence in situ hybridization, DNA microarrays, and proteomics. The main applications of these techniques in the modern management of acute leukemia, chronic myeloid leukemia, lymphomas, and breast cancer are summarized.

Blotting, Southern↗
PubMed2006

From molecular to modular cardiology. How to interpret the millions of data that came out from large scale analysis of gene expression?

Cell biology is in transition from reductionism, to a more integrated science which is now preoccupied by molecular interactions acting in modules. Large-scale quantitative analysis of gene expression, including cDNA microarrays and proteomic analysis, is now applied to heart failure and atherosclerosis. The technology is still at the beginning and is limited by variations in the array platforms and gene products as well as sensitivity or specificity of the selected probes. These limitations are progressively going to be reduced, but still they do exist. Biological systems are scale free networks made from genes, proteins or traits that interact one another and form networks and functional modules. Networks emerge through the addition of new nodes which are preferentially attached to more connected nodes to form hubs, according to the "rich-gets-richer" mechanism, and there are large networks which include central genes (nexus). Both hubs and nexus are attractive candidate for targeting new therapy. An important study from King JY et al. (Physiol Genomics 2005; 23: 103-18) exemplifies this concept by showing the first realistic pathways to understand atherosclerosis. The 4 steps of the design are based on histological grading and microarrays analysis and include an association network constructed from PubMed and the construction of sub-networks in which genes whose expression was differentially regulated were indicated. Connectivity analysis networks revealed new important modular pathways. In heart failure, no attempts have been made to organize the data into functional modulus. Since the causes of heart failure are well documented, the problem is to identify functional modules responsible for myocardial dysfunction. Several potential functional modules can be identified so far. Indeed, cardiac remodeling results from two types of changes in gene expression, namelly the reexpression of the foetal programme which has a mechanical origin and several well documented interfering determinants that modified the basic remodelling, including senescence, obesity, diabetes, ischemia, and the neurohormonal reaction.

Coronary Artery Disease↗