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At least 433 records · Page 24Linked to original sources

PubMed2006

[Exploration on correlation of syndrome proteomics and syndromatology of TCM].

The advent of the Post-Human Genome Project Era, represented by the raising of proteomics, would inevitably lead to the change of molecular biology from topical view to holistic with its thought turning from linear to complex mode. Based on the proteomics development in recent years, the authors summarized the methodology of TCM syndromatologic research, advocated in using two-dimensional gel electrophoresis (2-DE) and bioinformatics to identify different proteins. Proteomics should be led into the research of TCM syndrome categorization and the rule of evolution, which is necessary for researching the integration of the TCM study with proteomics and even with modern molecular biology based on molecular epidemiology level. Owing to the gradually developed coherence and mutual penetration of proteomics and TCM on the thinking method in studying life science, it has denoted the necessity and importance of integration of TCM and Western medicine in investigating the complex vital life phenomena.

Diagnosis, Differential↗

PubMed2025

Multi-Omics and Integrative Analytics in Natural Products Discovery.

Natural products (NPs) have long been an essential source of new bioactive compounds for drug discovery; however, traditional methods for screening and isolating these compounds can be slow and often yield diminishing returns. Fortunately, advanced multi-omics and computational approaches present powerful solutions to these challenges. This review highlights innovative methodologies that integrate metabolomics, genomics, transcriptomics, and proteomics with bioinformatics and analytical chemistry to accelerate NP discovery. For instance, untargeted metabolomics platforms like high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) and Global Natural Products Social (GNPS) molecular networking allow for comprehensive profiling of new compounds, while targeted isotope-labeling strategies enhance this process. Additionally, genome and metagenome mining tools such as antibiotics and secondary metabolite analysis shell (antiSMASH), Deep Biosynthetic Gene Cluster (DeepBGC), and Pipeline for Reconstructing Integrated Syntheses of Metabolites (PRISM) quickly identify biosynthetic gene clusters (BGCs) in both cultured and uncultured organisms, often using heterologous expression to validate products. Transcriptomic analyses, including RNA sequencing (RNA-seq), co-expression networks, and fluxomics, help clarify how pathways are regulated, while quantitative proteomics techniques like tandem mass tags/isobaric tags for relative and absolute quantitation (TMT/iTRAQ) and label-free methods, along with chemoproteomics approaches such as cellular thermal shift assay and thermal proteome profiling (TPP), uncover molecular targets and their mechanisms of action. This review also places significant emphasis on the role of artificial intelligence (AI) and machine learning (ML) in integrating multi-omics data, spanning activities from constructing gene-metabolite correlation networks to leveraging knowledge graphs and graph neural networks for data fusion and functional prediction. Finally, this review concludes by discussing the synergistic benefits of multi-omics for natural-product discovery, addressing current technical challenges, and exploring future directions toward high-throughput, intelligent data integration for next-generation NP research.

Biological Products↗

PubMed2006

Chemical effects in biological systems (CEBS) object model for toxicology data, SysTox-OM: design and application.

MOTIVATION: The CEBS data repository is being developed to promote a systems biology approach to understand the biological effects of environmental stressors. CEBS will house data from multiple gene expression platforms (transcriptomics), protein expression and protein-protein interaction (proteomics), and changes in low molecular weight metabolite levels (metabolomics) aligned by their detailed toxicological context. The system will accommodate extensive complex querying in a user-friendly manner. CEBS will store toxicological contexts including the study design details, treatment protocols, animal characteristics and conventional toxicological endpoints such as histopathology findings and clinical chemistry measures. All of these data types can be integrated in a seamless fashion to enable data query and analysis in a biologically meaningful manner. RESULTS: An object model, the SysBio-OM (Xirasagar et al., 2004) has been designed to facilitate the integration of microarray gene expression, proteomics and metabolomics data in the CEBS database system. We now report SysTox-OM as an open source systems toxicology model designed to integrate toxicological context into gene expression experiments. The SysTox-OM model is comprehensive and leverages other open source efforts, namely, the Standard for Exchange of Nonclinical Data (http://www.cdisc.org/models/send/v2/index.html) which is a data standard for capturing toxicological information for animal studies and Clinical Data Interchange Standards Consortium (http://www.cdisc.org/models/sdtm/index.html) that serves as a standard for the exchange of clinical data. Such standardization increases the accuracy of data mining, interpretation and exchange. The open source SysTox-OM model, which can be implemented on various software platforms, is presented here. AVAILABILITY: A universal modeling language (UML) depiction of the entire SysTox-OM is available at http://cebs.niehs.nih.gov and the Rational Rose object model package is distributed under an open source license that permits unrestricted academic and commercial use and is available at http://cebs.niehs.nih.gov/cebsdownloads. Currently, the public toxicological data in CEBS can be queried via a web application based on the SysTox-OM at http://cebs.niehs.nih.gov CONTACT: xirasagars@saic.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Computational Biology↗

PubMed2007

Toward an integrated microchip sized 2-D polyacrylamide slab gel electrophoresis device for proteomic analysis.

We describe a miniaturized instrument capable of performing 2-DE. Our miniaturized device is able to perform IEF and polyacrylamide slab gel electrophoresis (PASGE) in the same unit. It consists of a compartment for a first-dimensional IEF gel, which is connected to a second-dimensional PASGE gel. The focused samples are automatically transferred from the IEF gel to the PASGE gel by electromigration. Our preliminary experiments show that the device is able to focus and separate a mixture of proteins in approximately 1 h, excluding the time required for the staining procedure. On average, the gel-to-gel retardation factor (Rf) variation was 6.2% (+/-0.9%) and pI variation was 2.5% (+/-0.6%). Separated protein spots were excised from stained gels, digested with trypsin, and further identified by MS, thus enabling direct proteomic analysis of the separated proteins.

Electrophoresis, Gel, Two-Dimensional↗

PubMed2005

Apolipoprotein AI could be a significant determinant of epithelial integrity in rainbow trout gill cell cultures: a study in functional proteomics.

The freshwater fish gill forms a barrier against an external hypotonic environment. By culturing rainbow trout gill cells on permeable supports, as intact epithelia, this study investigates barrier property mechanisms. Under symmetrical conditions the apical and basolateral epithelial surfaces contact cell culture media. Replacing apical media with water, to generate asymmetrical conditions (i.e. the situation encountered by the freshwater gill), rapidly increases transepithelial resistance (TER). Proteomic analysis revealed that this is associated with enhanced expression of pre-apolipoprotein AI (pre-apoAI). To test the physiological relevance, gill cells were treated with a dose of 50 microg ml(-1) human apolipoprotein (apoAI). This was found to elevate TER in those epithelia which displayed a lower TER prior to apoAI treatment. These results demonstrate the action of apoAI and provide evidence that the rainbow trout gill may be a site of apoAI synthesis. TER does not differentiate between the trans-cellular (via the cell membrane) and para-cellular (via intercellular tight junctions) pathways. However, despite the apoAI-induced changes in TER, para-cellular permeability (measured by polyethylene glycol efflux) remained unaltered suggesting apoAI specifically reduces trans-cellular permeability. This investigation combines proteomics with functional measurements to show how a proteome change may be associated with freshwater gill function.

Animals↗

PubMed2024

PANAMA-enabled high-sensitivity dual nanoflow LC-MS metabolomics and proteomics analysis.

High-sensitivity nanoflow liquid chromatography (nLC) is seldom employed in untargeted metabolomics because current sample preparation techniques are inefficient at preventing nanocapillary column performance degradation. Here, we describe an nLC-based tandem mass spectrometry workflow that enables seamless joint analysis and integration of metabolomics (including lipidomics) and proteomics from the same samples without instrument duplication. This workflow is based on a robust solid-phase micro-extraction step for routine sample cleanup and bioactive molecule enrichment. Our method, termed proteomic and nanoflow metabolomic analysis (PANAMA), improves compound resolution and detection sensitivity without compromising the depth of coverage as compared with existing widely used analytical procedures. Notably, PANAMA can be applied to a broad array of specimens, including biofluids, cell lines, and tissue samples. It generates high-quality, information-rich metabolite-protein datasets while bypassing the need for specialized instrumentation.

Proteomics↗

PubMed2006

Technology Insight: renal proteomics--at the crossroads between promise and problems.

Knowledge of the human genome has fertilized research in the embryonic field of proteomics. The aim of this Review is to examine the recent application of emerging proteomic technologies to diagnosis of renal disease. We discuss the roles, efficacy and diagnostic potential of different proteomic approaches, focusing on current difficulties and potential solutions. Our rudimentary knowledge of the healthy human urine proteome is described, as are studies that have sought to use the urinary proteome as a tool for diagnosis of renal disease. Vignettes of renal proteome are also presented. The integral role of bioinformatics, and the need for standardized sample preservation and reporting of results, are discussed.

Humans↗

PubMedDate not supplied

CDNAs for functional genomics and proteomics: the German Consortium.

To functionally characterize numerous novel proteins encoded by cDNAs sequenced by the German Consortium, 800 were tagged with green fluorescent protein. The subcellular localizations of the fusion proteins were examined in living cells, enabling their classification in subcellular groups. Their activity in cell growth, cell death, and protein transport was screened in high throughput using robotic liquid handling and reading stations. The resulting information is integrated with functional genomics and proteomics data for further understanding of protein functions in the cellular context.

DNA, Complementary↗

PubMed2026

Integrating multi-omics technologies to decipher microbiome functions.

Multi-omics approaches have revolutionized our understanding of microbial communities by enabling simultaneous interrogation of genomic, transcriptomic, proteomic, and metabolomic data. The systematic integration and analysis of these deep datasets help decipher the functional roles of microbiomes, providing critical insights into microbial activities, interactions, and dynamics across diverse environments. Biological complexity makes multi-omics analysis of a single, isolated organism demanding but highly informative, yet this complexity increases further when samples comprise hundreds to thousands of individual species. As microbiome research continues to expand into clinical, environmental, and engineered systems, standardized workflows, benchmarked datasets, and community-driven initiatives are essential to ensure reproducibility, standardization and interpretability. Establishing and disseminating best practices for experimental design, data processing, and integrative analyses will be critical for maximizing comparability and scientific rigor across studies. This perspective highlights recent advances in multi-omics microbiome research, outlines key obstacles in data integration and metadata harmonization, and proposes a collaborative roadmap for scalable, FAIR-compliant multi-omics investigations and potentially disruptive Artificial Intelligence (AI) advances comparable to those of AlphaFold in the field of microbiome science.

Multiomics↗

PubMed2004

Use of proteomic analysis to monitor responses to biological therapies.

Proteomics has the potential to revolutionise diagnosis and disease management. Serum protein pattern profiling by surface-enhanced laser desorption/ionisation time of flight (SELDI-TOF) mass spectrometry is emerging as a novel approach to discover protein patterns capable of distinguishing disease and disease-free states with high sensitivity and specificity. This method has shown great promise for early diagnosis of ovarian cancer and is being applied to a range of pathological states. Protein microarray technology is being evaluated as a new means to track biological responses to therapy. Through the measurement of key protein phosphorylation sites at different stages of disease progression or before and after treatment, protein signal pathways can be mapped and thus become the starting point for individualised therapy. Laser capture microdissection (LCM) coupled with immunostaining of protein microarrays allows isolation of pure cell populations and relative quantitation of phosphorylated and non-phosphorylated forms of the cell's key signalling proteins. This technology is currently in use at the National Institutes of Health in Phase II clinical trials of metastatic breast and ovarian cancer. Cell survival and apoptotic protein pathways are monitored as biological markers of disease progression in these clinical trials. Proteomic technologies, such as serum protein pattern profiling, combined with protein microarray technologies, constitute a new paradigm for detecting disease and monitoring disease response to therapy. Ultimately, proteomics and genomics will become integrated into cancer patient management through the design and tracking of individualised therapy.

Animals↗

PubMed2026

Pilot study identifying distinct circulating proteomic profiles associated with longitudinal CT-defined fibrotic and inflammatory sarcoidosis.

INTRODUCTION: Pulmonary sarcoidosis exhibits heterogeneous clinical trajectories ranging from self-limited disease resolution to chronic progressive fibrosis, yet reliable biomarkers capable of distinguishing these disease patterns remain lacking. Whether longitudinal CT-defined sarcoidosis phenotypes are associated with distinct circulating molecular signatures remains unknown. METHODS: We performed high-throughput plasma proteomics (SomaScan 11K) in participants with pulmonary sarcoidosis classified into longitudinal chest CT-defined progressive fibrosis, progressive nodular inflammatory disease, or resolving disease trajectories, along with healthy controls. CT phenotypes were assigned based on predefined longitudinal changes in reticulation, traction bronchiectasis, nodular involvement, and mediastinal lymphadenopathy across serial CT scans. One plasma sample per participant was selected from the study visit corresponding to the CT time point at which criteria for the assigned longitudinal phenotype were met. Principal component analysis, hierarchical clustering, pathway enrichment, and correlation-based analyses linking protein expression to quantitative CT features were used to evaluate whether distinct longitudinal CT phenotypes were associated with divergent proteomic signatures. RESULTS: Principal component analysis and hierarchical clustering suggested partial segregation by CT-defined phenotype. Longitudinal CT phenotypes were associated with distinct pathway-level proteomic signatures, with progressive fibrosis enriched for epithelial-mesenchymal transition signaling, and progressive nodular inflammatory disease enriched for mTORC1, MYC, oxidative phosphorylation, adipogenesis, and fatty acid metabolism pathways. Correlation analyses showed coordinated protein-expression patterns associated with fibrotic CT features and mediastinal lymph node enlargement. DISCUSSION: These findings suggest that longitudinal CT-defined fibrotic and inflammatory sarcoidosis phenotypes are associated with distinct pathway-level proteomic signatures. This pilot study provides preliminary proof-of-concept evidence that integrating longitudinal CT imaging phenotypes with plasma proteomics may serve as a framework for future mechanistic studies and biomarker discovery in pulmonary sarcoidosis.

Humans↗

PubMed2006

InSilicoSpectro: an open-source proteomics library.

We present a new proteomics open-source project, InSilicoSpectro, aimed at implementing recurrent computations that are necessary for proteomics data analysis. Illustrative examples are mass list file format conversions, protein sequence digestion, theoretical peptide and fragment mass computations, graphical display, matching with experimental data, isoelectric point estimation, and peptide retention time prediction. The project library is written in Perl, a widely used scripting language in bioinformatics, and it offers a unique framework of integrated objects to implement complex proteomics data analyses. For instance, only a few lines of code are required to digest a protein with fixed and variable modifications, label peptides with 18O, compute the fragmentation spectra and display their match with experimental spectra. We believe that InSilicoSpectro will be of great help to bioinformaticians, without detailed knowledge of proteomics specifics, and to mass spectrometrists with computer programming interest as well.

Amino Acid Sequence↗

PubMed1999

Urological malignancies and the proteomic-genomic interface.

The urological malignancies, renal, bladder and prostate cancer, account for approximately 16% of all cancer cases. Unfortunately 5-year survival rates are relatively poor, largely a result of many cases not being diagnosed before the tumour has metastasised. There is a clear need for the identification of markers which will allow earlier detection of disease, and predict prognosis and response to therapy. In addition, they may be of use as therapeutic targets. Current advances in molecular biology are allowing the identification of a number of tumour-associated changes which could be of clinical use in the future. However, with the rapid technological advances being made in the field of proteomics, this approach could be integrated with genomics providing a complementary alternative, overcoming disparities between mRNA levels and protein production, and additionally allowing the identification of tumour-associated post-translational modifications. These approaches have already been used to identify novel genes and other cancer-related changes involved in the pathogenesis of urological malignancies. This review describes current progress in the genomic and proteomic study of urological malignancies, and highlights the potential of using proteomic technologies in the study of this group of diseases.

Genome↗

PubMed2003

At the crossroads of chemistry and biology.

The life sciences are molecular and the harnessing of information gleaned from genomics and proteomics will require interdisciplinary research integrating chemistry and biology. This approach is illustrated by the synthesis and biological evaluation of lipidated peptides and proteins and the delineation of a concept arguing for natural product guided combinatorial chemistry.

Biochemistry↗

PubMed2026

Moving Beyond Morphology to Multiplexed Molecular Imaging as the Next Frontier in Diagnostic Pathology.

Diagnostic pathology has long relied on the morphologic interpretation of hematoxylin and eosin-stained tissues to guide diagnosis and assess prognostic features. Although pathologists intuitively recognize spatial patterns and architectural organization, these assessments remain largely qualitative and difficult to quantify systematically. Immunohistochemistry and immunofluorescence have introduced molecular specificity but are limited in multiplexing capacity, whereas bulk genomic and transcriptomic assays provide high molecular depth but lose spatial context by averaging signals across heterogeneous cell populations. Recent advances in spatial proteomics-including mass spectrometry-based imaging and cyclic immunofluorescence-now enable multiplexed, single-cell protein analysis within intact tissue architecture. These technologies have revealed complex immune and stromal microenvironments, spatially organized biomarkers predictive of therapeutic response, and molecular gradients underlying disease progression. By integrating histologic and molecular information, spatial proteomics bridges traditional microscopy with high-dimensional omics, allowing quantitative, spatially resolved insights into tissue organization and disease mechanisms. This review summarizes recent developments in multiplexed spatial proteomics from both scientific and pathologic perspectives, highlighting how these technologies extend beyond morphology to quantify histologic patterns, refine biomarker discovery, and facilitate clinical translation. The review also examines translational challenges and barriers to clinical implementation, including costs, standardization requirements, and workflow integration.

Humans↗

PubMed2002

Bioinformatics, functional genomics, and proteomics study of Bacillus sp.

The ability of bioinformatics to characterize genomic and proteomic sequences from bacteria Bacillus sp. for prediction of genes and proteins has been evaluated. Genomics coupling with proteomics, which is relied on integration of the significant advances recently achieved in two-dimensional (2-D) electrophoretic separation of proteins and mass spectrometry (MS), are now important and high throughput techniques for qualifying and analyzing gene and protein expression, discovering new gene or protein products, and understanding of gene and protein functions including post-genomic study. In addition, the bioinformatics of Bacillus sp. is embraced into many databases that will facilitate to rapidly search the information of Bacillus sp. in both genomics and proteomics. It is also possible to highlight sites for post-translational modifications based on the specific protein sequence motifs that play important roles in the structure, activity and compartmentalization of proteins. Moreover, the secreted proteins from Bacillus sp. are interesting and widely used in many applications especially biomedical applications that are the highly advantages for their potential therapeutic values.

Bacillus↗

PubMed2004

An XML standard for the dissemination of annotated 2D gel electrophoresis data complemented with mass spectrometry results.

BACKGROUND: Many proteomics initiatives require a seamless bioinformatics integration of a range of analytical steps between sample collection and systems modeling immediately assessable to the participants involved in the process. Proteomics profiling by 2D gel electrophoresis to the putative identification of differentially expressed proteins by comparison of mass spectrometry results with reference databases, includes many components of sample processing, not just analysis and interpretation, are regularly revisited and updated. In order for such updates and dissemination of data, a suitable data structure is needed. However, there are no such data structures currently available for the storing of data for multiple gels generated through a single proteomic experiments in a single XML file. This paper proposes a data structure based on XML standards to fill the void that exists between data generated by proteomics experiments and storing of data. RESULTS: In order to address the resulting procedural fluidity we have adopted and implemented a data model centered on the concept of annotated gel (AG) as the format for delivery and management of 2D Gel electrophoresis results. An eXtensible Markup Language (XML) schema is proposed to manage, analyze and disseminate annotated 2D Gel electrophoresis results. The structure of AG objects is formally represented using XML, resulting in the definition of the AGML syntax presented here. CONCLUSION: The proposed schema accommodates data on the electrophoresis results as well as the mass-spectrometry analysis of selected gel spots. A web-based software library is being developed to handle data storage, analysis and graphic representation. Computational tools described will be made available at http://bioinformatics.musc.edu/agml. Our development of AGML provides a simple data structure for storing 2D gel electrophoresis data.

Computational Biology↗

PubMed2005

Transfusion medicine in the era of genomics and proteomics.

Viewing recent trends in transfusion medicine (TM), the authors make predictions about possible future developments within this specialty including greater cost-effectiveness and blood safety resulting from increased automation; techniques in genetics replacing serological typing in many standard assays; and TM service playing a major R&D role together with clinical services in the emerging cell-based therapeutics. To achieve this, the TM laboratory of the future will need to have available extensive skills in immunogenetics and database expertise; emerging techniques in genomics and proteomics will need to be integrated with classic immunohematology approaches; and collaborative networks of TM laboratories will need to raise their profiles as a competent partner in the ongoing clinical biotechnology revolution. Blood product safety is profiled to highlight some of these developments. Until recently, avoiding pathogen transmission has focused primarily on excluding at-risk donors and testing donor blood for pathogen markers. Newer trends in pathogen-inactivation procedures could alter the protein composition of the blood product, potentially causing unintended immune reactions that could outweigh their benefits in further reducing a very low current risk of pathogen transmission. By combining proteomics and immunohematology, those manufacturing processes least likely to generate posttranslational protein modifications will need to be identified.

Automation↗