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PubMed2004

Broad-based proteomic strategies: a practical guide to proteomics and functional screening.

Proteomics, the study of the proteome (the collection of all the proteins expressed from the genome in all isoforms, polymorphisms and post-translational modifications), is a rapidly developing field in which there are numerous new and often expensive technologies, making it imperative to use the most appropriate technology for the biological system and hypothesis being addressed. This review provides some guidelines on approaching a broad-based proteomics project, including strategies on refining hypotheses, choosing models and proteomic approaches with an emphasis on aspects of sample complexity (including abundance and protein characteristics), and separation technologies and their respective strengths and weaknesses. Finally, issues related to quantification, mass spectrometry and informatics strategies are discussed. The goal of this review is therefore twofold: the first section provides a brief outline of proteomic technologies, specifically with respect to their applications to broad-based proteomic approaches, and the second part provides more details about the application of these technologies in typical scenarios dealing with physiological and pathological processes. Proteomics at its best is the integration of carefully planned research and complementary techniques with the advantages of powerful discovery technologies that has the potential to make substantial contributions to the understanding of disease and disease processes.

Animals↗

PubMed2026

Characterizing the impact of plasma protein levels on human brain structure and disorders leveraging integrative multi-omics analysis.

With recent advances in high-throughput proteomic technologies, population-scale plasma proteomics datasets, often linked to extensive genetic and phenotypic information, have become increasingly accessible. Yet the relationships between circulating protein levels, brain imaging phenotypes, and risk for neurological and psychiatric disorders remain largely unexplored. Proteome-wide association studies offer a promising approach for elucidating biological mechanisms that connect genetic variation to complex brain-related traits and diseases. In this study, we integrated protein quantitative trait loci (pQTLs) from the two largest plasma proteomic resources (the UK Biobank Pharma Proteomics Project [UKB-PPP] and Ferkingstad et al. [deCODE]) with genome-wide association studies of brain imaging-derived phenotypes in UK Biobank using Mendelian randomization and colocalization analyses. We identified 120 cis and 20 trans associations between plasma proteins and imaging phenotypes and validated these findings using brain tissue-derived proteomic and transcriptomic datasets. Multivariable Mendelian randomization revealed eleven plasma proteins (coding genes APOE, ARL3, MICB, NSF, RHOC, RSPO3, ENPP2, BTN2A1, EIF2AK3, MRVI1, and OPLAH) with significant direct effects on the risk of Alzheimer's disease, Parkinson's disease, multiple sclerosis, bipolar disorder, and schizophrenia. Single-cell expression and pathway enrichment analyses further revealed cell-type-specific effects and distinct biological processes underlying these protein-disease associations. Together, these findings demonstrate robust links between plasma protein variation and brain structure, delineate protein-disease pathways, and highlight the cellular and molecular mechanisms that contribute to neurobiological diversity and pathology.

Journal Article↗

PubMed2003

Proteomics.

Fueled by ever-growing DNA sequence information, proteomics-the large scale analysis of proteins-has become one of the most important disciplines for characterizing gene function, for building functional linkages between protein molecules, and for providing insight into the mechanisms of biological processes in a high-throughput mode. It is now possible to examine the expression of more than 1000 proteins using mass spectrometry technology coupled with various separation methods. High-throughput yeast two-hybrid approaches and analysis of protein complexes using affinity tag purification have yielded valuable protein-protein interaction maps. Large-scale protein tagging and subcellular localization projects have provided considerable information about protein function. Finally, recent developments in protein microarray technology provide a versatile tool to study protein-protein, protein-nucleic acid, protein-lipid, enzyme-substrate, and protein-drug interactions. Other types of microarrays, though not fully developed, also show great potential in diagnostics, protein profiling, and drug identification and validation. This review discusses high-throughput technologies for proteome analysis and their applications. Also discussed are the approaches used for the integrated analysis of the voluminous sets of data generated by proteome analysis conducted on a global scale.

Drug Design↗

PubMedDate not supplied

Data-dependent electron capture dissociation FT-ICR mass spectrometry for proteomic analyses.

Electron capture dissociation (ECD) offers many benefits for the analysis of peptides and proteins, and consequently shows great potential for the field of proteomics. Recent developments have reduced the time scale required for ECD to milliseconds resulting in the technique's compatibility with on-line separation techniques, e.g., HPLC. Here, we demonstrate incorporation of ECD into a high-throughput data-dependent LC-MS/MS approach for the analysis of proteomic samples. The approach is applied to analysis of the protein Fc-ROR2 isolated from chondrocytes and is the first example of LC-ECD-MS/MS of such a sample. Protein sequence coverage was 29%. Within that coverage, fifteen peptides were isolated and subjected to ECD. In most cases, the sequence tag generated by ECD was over 70% (in terms of the number of peptide backbone cleavages). The ECD data were searched against the nonredundant human NCBI database using the SEQUEST algorithm. Protein ROR2 was assigned, as was IgG (Fc domain). The results demonstrate the suitability of ECD as an integral technique in high-throughput proteomic strategies.

Algorithms↗

PubMed2002

H. influenzae Consortium: integrative study of H. influenzae-human interactions.

Developments in high-throughput analysis tools coupled with integrative computational techniques have enabled biological studies to reach new levels. The ability to correlate large volumes of diverse data types into cohesive models of organism function has spawned a new systematic approach to biological investigation. The creation of a new consortium has been proposed to investigate a single organism utilizing these comprehensive approaches. The Haemophilus influenzae Consortium (HIC) would be comprised of five laboratories, each providing separate and complementary areas of expertise in the study of Haemophilus influenzae (HI). The 5-year study proposes to develop coherent models of HI, both as a stand-alone organism, and more importantly, as a human pathogen. Studies in growth condition specificity followed by genomic, metabolic, and proteomic experimentation will be combined and integrated through computational and experimental analyses to form dynamic and predictive models of HI and its responses. Data from the HIC will allow greater understanding of cellular behavior, pathogen-host interactions, bacterial infection, and provide future scientific endeavors with a template for studies of other pathogens.

Bacterial Adhesion↗

PubMed2002

Infectomics: genomics and proteomics of microbial infections.

The completion of genomic sequences is the greatest triumph of molecular reductionism since the discovery of the DNA double helix in 1953. However, the utility of reductionism is becoming limited and holistic approaches, including theories and techniques, are desperately needed in the postgenomic era. In the field of infectious diseases there is an urgent need for global approaches that can efficiently, precisely and integratively study structural and functional genomics and proteomics of microbial infections (infectomics). The combination of new (e.g. DNA and protein microarrays) and traditional approaches (e.g. cloning, PCR, gene knockout and knockin, and antisense) will help overcome the challenges we are facing today. We assume that the global phenotypic changes (infectomes) in microbes and their host during infections are encoded by the genomes of microbial pathogens and their hosts, expressed in certain environmental conditions devoted to specific microbe-host interactions. Global drug responses (pharmacomes) in microbes and their host can be detected by genomic and proteomic approaches. Genome-wide approaches to genotyping and phenotyping or expression profiling will eventually lead to global dissection of microbial pathogenesis, efficient and rapid diagnosis of infectious diseases, and the development of novel strategies to control infections. The key fundamental issue of infectious diseases is how to globally and integratively understand the interactions between microbial pathogens and their hosts by using infectomics. In this review, we focus on the events that are considered important in infectomics.

Animals↗

PubMed2004

Biomedical informatics: development of a comprehensive data warehouse for clinical and genomic breast cancer research.

The Windber Research Institute is an integrated high-throughput research center employing clinical, genomic and proteomic platforms to produce terabyte levels of data. We use biomedical informatics technologies to integrate all of these operations. This report includes information on a multi-year, multi-phase hybrid data warehouse project currently under development in the Institute. The purpose of the warehouse is to host the terabyte-level of internal experimentally generated data as well as data from public sources. We have previously reported on the phase I development, which integrated limited internal data sources and selected public databases. Currently, we are completing phase II development, which integrates our internal automated data sources and develops visualization tools to query across these data types. This paper summarizes our clinical and experimental operations, the data warehouse development, and the challenges we have faced. In phase III we plan to federate additional manual internal and public data sources and then to develop and adapt more data analysis and mining tools. We expect that the final implementation of the data warehouse will greatly facilitate biomedical informatics research.

Breast Neoplasms↗

PubMed2026

Multi-omic signatures of genetic mechanisms inform on type 2 diabetes biology and patient heterogeneity.

Type 2 diabetes (T2D) is a heterogeneous disease shaped by genetic pathways related to insulin resistance and β-cell dysfunction, but how this heterogeneity is reflected molecularly remains unclear. We integrated partitioned polygenic scores (pPS) with proteomic and metabolomic profiling to define molecular signatures of T2D and their clinical relevance. We analyzed UK Biobank participants with genomic, proteomic, and metabolomic data. In a disease-free training subset, we used LASSO regression to identify multi-omic signatures associated with each pPS by jointly modeling proteins and metabolites. In an independent testing set, we constructed multi-omic scores and examined their associations with clinical traits and diabetes-related outcomes. Mediation analyses were used to investigate putative causal pathways. Key findings were evaluated in the Multi-Ethnic Study of Atherosclerosis (MESA). We identified distinct multi-omic signatures that capture the molecular architecture of T2D genetic risk across physiological subtypes. Compared with genetic scores alone, multi-omic pPS showed larger effect sizes and better disease discrimination. These scores recapitulated subtype-specific physiology and were associated with T2D risk. The Beta-Cell 2 multi-omic score showed marked stratification for insulin use, which was replicated in MESA, where it also predicted future insulin use. Mediation analyses implicated lipoprotein remodeling and fatty acid metabolism in the Lipodystrophy 1 cluster, accounting for 30-45% of the total effect of pPS on T2D risk. Integrating process-specific genetic risk with circulating multi-omic profiles reveals biologically distinct endotypes of T2D and supports a framework for improved patient stratification and risk assessment.

Journal Article↗

PubMed2005

SELDI-TOF-MS proteomics of breast cancer.

The detection, diagnosis, and management of breast cancer rely on an integrated approach using clinical history, physical examination, imaging, and histopathology. The discovery and validation of novel biomarkers will aid the physician in more effectively achieving this integration. This review discusses efforts in surface-enhanced laser desorption/ionization (SELDI)-based proteomics to address various clinical questions surrounding breast cancer, including diagnosis, monitoring, and stratification for treatment. Emphasis is placed on examining how study design and execution influence the discovery and validation process, which is critical to the proper development of potential clinical tests.

Biomarkers, Tumor↗

PubMed2002

Sequential fractionation and two-dimensional gel analysis unravels the complexity of the dimorphic fungus Candida albicans cell wall proteome.

The cell wall proteins of Candida albicans play a key role in morphogenesis and pathogenesis and might be potential target sites for new specific antifungal drugs. However, these proteins are difficult to analyze because of their high heterogeneity, interconnections with wall polysaccharides (mannan, glucan, and chitin), low abundance, low solubility, and hydrophobic nature. Here we report a subproteomic approach for the study of the cell wall proteins (CWPs) from C. albicans yeast and hyphal forms. Most of the mannoproteins present in this compartment were extracted by cell wall fractionation according to the type of interactions that they establish with other structural components. CWPs were solubilized from isolated cell walls by hot SDS and dithiothreitol treatment followed by extraction either by mild alkali conditions or by enzymatic treatment with glucanases and chitinases. These highly enriched cell wall fractions were analyzed by two-dimensional PAGE, showing that a large number of proteins are involved in cell wall construction and that the wall remodeling that occurs during germ tube formation is related to changes in the composition of CWPs. We suggest that the CWP-chitin linkage is an important retention mechanism of CWPs in C. albicans mycelial forms. This article also highlights the usefulness of the combination of sequential fractionation and two-dimensional PAGE followed by Western blotting using specific antibodies against known CWPs in the characterization of incorporation mechanisms of such CWPs into the cell wall and of their interactions with other wall components. Mass spectrometry analyses have allowed the identification of several cell surface proteins classically associated with both the cell wall and other compartments. The physiological significance of the dual location of these moonlighting proteins is also discussed. This approach is therefore a powerful tool for obtaining a comprehensive and integrated view of the cell wall proteome.

Blotting, Western↗

PubMed2006

TMABoost: an integrated system for comprehensive management of tissue microarray data.

In the last decade, high-throughput technologies such as DNA and tissue microarrays (TMAs) have become a means of large-scale investigation of gene expression, providing a plethora of new biomedical data in a relatively short time. Data collection and organization are critical aspects in this process to ensure the quality and reliability of future data interpretation. In this work, we propose a comprehensive approach to handle TMA data with the aim of supporting and promoting biomarker development. We describe a web-based system for the complete management of tissue microarray data in the field of pathology. The system has been in use since June, 2003. Our approach includes automatic localization and identification of tissue microarray samples, and quantitative image analysis that allows high-throughput screening of TMAs by ensuring nonsubjective measures and novel prognosis associations. In this paper, we present the architecture and the components of this system.

Algorithms↗

PubMed2005

WebGestalt: an integrated system for exploring gene sets in various biological contexts.

High-throughput technologies have led to the rapid generation of large-scale datasets about genes and gene products. These technologies have also shifted our research focus from 'single genes' to 'gene sets'. We have developed a web-based integrated data mining system, WebGestalt (http://genereg.ornl.gov/webgestalt/), to help biologists in exploring large sets of genes. WebGestalt is composed of four modules: gene set management, information retrieval, organization/visualization, and statistics. The management module uploads, saves, retrieves and deletes gene sets, as well as performs Boolean operations to generate the unions, intersections or differences between different gene sets. The information retrieval module currently retrieves information for up to 20 attributes for all genes in a gene set. The organization/visualization module organizes and visualizes gene sets in various biological contexts, including Gene Ontology, tissue expression pattern, chromosome distribution, metabolic and signaling pathways, protein domain information and publications. The statistics module recommends and performs statistical tests to suggest biological areas that are important to a gene set and warrant further investigation. In order to demonstrate the use of WebGestalt, we have generated 48 gene sets with genes over-represented in various human tissue types. Exploration of all the 48 gene sets using WebGestalt is available for the public at http://genereg.ornl.gov/webgestalt/wg_enrich.php.

Computer Graphics↗

PubMed2002

Complexities in ETS-domain transcription factor function and regulation: lessons from the TCF (ternary complex factor) subfamily. The Colworth Medal Lecture.

The ETS-domain transcription factor family can be divided into a series of subfamilies. Elk-1 represents the founding member of the ternary complex factor (TCF) subfamily. By focusing on the TCF subfamily, we can demonstrate the complexities that exist in the function and regulation of ETS-domain transcription factors. This article focuses on Elk-1 in detail and summarizes the functions of other TCFs. The key themes covered include the domain structure of the TCFs, the mechanisms of complex formation with serum response factor, regulation of TCFs by mitogen-activated protein kinase cascades, and transcriptional regulatory properties of the TCFs. Finally, the emerging role of the TCFs in vivo is discussed. A picture is developing indicating that, while these proteins exhibit significant sequence and functional conservation, key differences in their structure and regulation are being identified which may relate to unique functions of these proteins in vivo.

Amino Acid Sequence↗

PubMedDate not supplied

Modified immobilized pH gradient gel strip equilibration procedure in SWISS-2DPAGE protocols.

In the present paper we report a revised protocol for immobilized pH gradient (IPG) gel strip equilibration involving a procedural modification between the first- and second-dimensional separation in both analytical and preparative two-dimensional polyacrylamide gel electrophoresis (2-D PAGE). By changing the pH of the equilibration buffer (pH 8.0), the concentration of alkylating reagent (125 mM iodoacetamide) and the time of incubation (15 min), it has been possible to achieve increased cysteine (Cys) alkylation to completion with only one adduct of carboxyamidomethyl-Cys formed. Importantly, the modification does not alter the 2-D proteome patterns and therefore maintains the integrity of the existing SWISS-2DPAGE entries. Results are presented for comparative analyses using human plasma, and for Cys analysis of human albumin to illustrate the advantages of the improved protein reduction and Cys alkylation. The modified step of IPG gel strip equilibration will assist protein digestion for matrix-assisted laser desorption/ionisation - time-of-flight - mass spectrometry analysis, and make Cys quantitation possible without further in-gel or on-blot alkylation.

Blood Proteins↗

PubMed2026

Region-Resolved Integrative Multi-Omic Characterization Reveals Diverse Tumor and Microenvironment Features of Pituitary Neuroendocrine Tumors.

Pituitary neuroendocrine tumors are frequently invasive, with cavernous sinus invasion leading to poor treatment outcomes and high recurrence. Regional differences within these tumors remain poorly understood, hindering targeted therapy development. Here, we present the first integrative multi-omics analysis combining proteomics, metabolomics and single-cell transcriptomics to characterize tumors from the cavernous sinus and saddle regions. Our results reveal profound regional and cellular heterogeneity: cavernous sinus tumors exhibit significantly enhanced cell proliferation, driven by cancer-associated fibroblasts through the IGF1-IGF1R-MAPK1 axis. Cancer-associated fibroblasts in the cavernous sinus secrete IGF1 under regulation of the transcription factor FOXO1, which binds to receptors on tumor cells to activate proliferation. Metabolomic profiling identifies proline as a key enriched metabolite that stimulates cancer-associated fibroblasts to produce collagen fibers, reinforcing a pro-tumorigenic microenvironment. Single-cell transcriptomics further delineates a distinct subpopulation of receptor-positive malignant cells and a high abundance of cancer-associated fibroblasts in the cavernous sinus. These findings establish core mechanisms underlying the aggressive behavior of cavernous sinus-invading tumors, providing novel actionable targets for precision therapeutic strategies tailored to distinct tumor regions.

Humans↗

PubMed2025

Integrated Multiomics Analysis of Microsatellite Instability-High Colorectal Cancer Identifies a Subtype With Poor Outcome.

Up to 50% of patients with metastatic microsatellite instability-high (MSI-H) colorectal cancer (CRC) are resistant to immunotherapy and experience progression or recurrence after treatment. We integrated the genomic, epigenomic, transcriptomic, and proteomic data for 99 patients in a Chinese MSI-H CRC cohort. Proteomic profiling of primary tumors clearly classified MSI-H tumors into 2 subtypes. We found that the 2 subtypes have different mutational signatures, enriched pathways, gene fusion networks, and clinical outcomes. Notably, NCAM1 could serve as a potential biomarker for checkpoint inhibitor response in MSI-H CRC. Thus, there is an urgent need to stratify the MSI-H group into different subtypes and adopt more targeted therapies to prolong patient survival.

Humans↗

PubMed2006

Genetic approaches to the improvement of fertility traits in the pig.

One of the major determinants for litter size in pigs is prenatal mortality. It occurs most frequently during the first few weeks of gestation and can be attributed to abnormalities in developmental processes during embryogenesis including trophoblastic elongation and blastocyst implantation. Improvement of litter size has been attempted by means of phenotypic selection. However, another promising approach in pursuit of this aim has been the use of genotypic information. Reproductive traits in general are well-suited for application of marker-assisted selection (MAS). The possibility of exerting selection criteria at the molecular level shortens the generation interval as the selection decision can take place early in the life of an animal. Moreover, in consideration of the sex-limited nature of reproductive traits, genotypic information allows for selection in the gender in which the trait cannot be directly observed. Accordingly, there has been considerable interest in mapping and identifying genes involved in the regulation of reproductive traits and in elucidating their expression patterns. This review offers a comprehensive, if not exhaustive, account of the efforts being made and the approaches currently used in this field. One approach has been to choose candidate genes a priori because of the physiological importance of the proteins they encode and their role in the reproduction of other mammals. The usefulness of candidate genes is then examined by association studies between genetic polymorphisms identified in the respective candidate genes and the phenotypic reproductive traits. The other approach discussed uses pre-existing or designed families for linkage analyses in order to map the location of quantitative trait loci (QTL) for the reproductive trait of interest. The results reported were not consistent among different studies but the QTL regions detected may be useful for identification of positional candidate genes in further molecular genetic studies. However, a better understanding of porcine reproduction requires that these functional genomic approaches are merged and integrated with detailed analyses of the proteome to establish linkages between predisposition and physiology.

Animals↗

PubMed2002

Acid-labile isotope-coded extractants: a class of reagents for quantitative mass spectrometric analysis of complex protein mixtures.

Quantitative mass spectrometry using stable isotope-labeled tagging reagents such as isotope-coded affinity tags has emerged as a powerful tool for identification and relative quantitation of proteins in current proteomic studies. Here we describe an integrated approach using both automated two-dimensional liquid chromatography/ mass spectrometry (2D-LC/MS) and a novel class of chemically modified resins, termed acid-labile isotope-coded extractants (ALICE), for quantitative mass spectrometric analysis of protein mixtures. ALICE contains a thiol-reactive group that is used to capture all cysteine (Cys)-containing peptides from peptide mixtures, an acid-labile linker, and a nonbiological polymer. The acid-labile linker is synthesized in both heavy and light isotope-coded forms and therefore enables the direct relative quantitation of peptides/proteins through mass spectrometric analysis. To test the ALICE method for quantitative protein analysis, two model protein mixtures were fully reduced, alkylated, and digested in solution separately and then Cys-containing peptides covalently captured by either light or heavy ALICE. The reacted light and heavy ALICE were mixed and washed extensively under rigorous conditions and the Cys-containing peptides retrieved by mild acid-catalyzed elution. Finally, the eluted peptides were directly subjected to automated 2D-LC/MS for protein identification and LC/MS for accurate relative quantitation. Our initial study showed that quantitation of protein mixtures using ALICE was accurate. In addition, isolation of Cys-containing peptides by the ALICE method was robust and specific and thus yielded very low background in mass spectrometric studies. Overall, the use of ALICE provides improved dynamic range and sensitivity for quantitative mass spectrometric analysis of peptide or protein mixtures.

Amino Acid Sequence↗