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PubMed2005

Proteomic biomarker analysis of amniotic fluid for identification of intra-amniotic inflammation.

BACKGROUND: Intra-amniotic inflammation is associated with poor neonatal outcome independent of prematurity. We applied proteomic technology (SELDI: surface-enhanced laser desorption ionisation) to identify the proteomic profile of intra-amniotic inflammation. DESIGN: One hundred and four samples of amniotic fluid were analysed. In stage 1, samples from patients with symptoms of preterm labour and known outcomes were tested to identify the characteristic profile for inflammation. We extracted the profile using a novel, stepwise logical approach comparing SELDI tracings from patients who delivered preterm and had intra-amniotic inflammation in response to infection to the tracings of patients who had symptoms of preterm labour but delivered at term. In stage 2, we applied the algorithm to samples from pregnancies whose outcomes were unknown to the investigators. SETTING: North-American university in collaboration with Ciphergen field demonstration laboratory. SAMPLE: One hundred and four samples of human amniotic fluid from transabdominal amniocentesis. METHODS: SELDI (surface-enhanced laser desorption ionisation) and Mass Restricted analysis, a novel algorithm for extraction of clinical and biological relevant biomarkers from proteomic SELDI tracings. MAIN OUTCOME MEASURE: Presence of intra-amniotic inflammation and/or infection leading to preterm birth. RESULTS: Patients with intra-amniotic inflammation that deliver preterm have a distinctive amniotic fluid proteomic profile of three or four of the following proteins: neutrophil defensins-1 and -2, and calgranulins A and C. Based on the presence or absence of these biomarkers, we devised the mass restricted (MR) score ranging from 0 (all biomarker peaks absent) to 4 (all biomarker peaks present). In stage 1, MR score > 2 had 92.9% sensitivity (95% CI 76.5-98.9) and 91.8% specificity (95% CI 80.4-97.7) for detection of intra-amniotic inflammation. In blind testing (stage 2), MR score > 2 provided 100% specificity and sensitivity (95% CI 100-100). A MR score > 2 was associated with imminent preterm delivery. CONCLUSION: Proteomic analysis of amniotic fluid reveals the presence of biomarkers characteristic of intrauterine inflammation. This methodology may identify the subgroup of patients that might benefit most from interventions to prevent fetal damage in utero.

Adult↗

PubMed2026

Comparison of Proteomic Analysis of Cerebrospinal Fluid From Neurological Patients With and Without Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder characterised by progressive muscle weakness in both bulbar and extremity muscles, leading to a diverse clinical phenotype with motor and non-motor symptoms. Approximately 85% of ALS cases are sporadic (sALS), while the remaining 10%-15% are familial (fALS). Biological biomarkers of sporadic ALS remain poorly understood, hindering precise patient screening, delaying diagnosis and negatively affecting prognosis. This study aims to identify potential proteomic biomarkers by comparing the cerebrospinal fluid (CSF) of sALS patients with that of patients suffering from other neurological diseases. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used for proteomic profiling of CSF samples from 24 sALS patients and 26 patients with other neurological diseases. The complete protein expression profiles were compared using a two-tailed Student's t-test, with a p < 0.05 considered statistically significant with additional FDR correction at the 0.1 level. Proteomic analysis of CSF samples identified significant quantitative changes in 96 proteins with threshold p < 0.05 and 74 proteins with FDR < 0.1 between sALS and non-ALS patients, including alterations in proteins associated with neurodegenerative processes, such as amyloid precursor proteins and inflammatory markers. CSF proteomic analysis reveals altered inflammatory and neurodegenerative metabolic pathways, providing valuable insights into the proteomic landscape of sALS. Several dysregulated proteins were consistent with the disease mechanisms highlighted in previous studies. These findings represent a step forward in developing personalised approaches for diagnosing and managing the disease.

Humans↗

PubMed2006

Quantitative proteomics and its applications for systems biology.

Here we discuss the current state of research in the rapidly growing field of quantitative proteomics and its applications to systems biology. Quantitative proteomics can be successfully used for characterizing alterations in protein abundance, finding novel protein-protein and protein-peptide interactions, investigating formation of large macromolecular complexes, and elucidating temporal changes in organellar protein composition and phosphorylation in signal transduction cascades. Further, quantitative proteomics can directly compare activation of entire signaling networks in response to individual stimuli and discover critical differences in their circuits that account for alterations of cell response. Maturation of proteomic bioinformatics applications and continuous improvements in proteomics and related genomics and transcriptomics technologies now allows us to investigate cellular mechanisms at the integrative system level.

Animals↗

PubMed2004

Molecular classification and survival prediction in human gliomas based on proteome analysis.

The biological features of gliomas, which are characterized by highly heterogeneous biological aggressiveness even in the same histological category, would be precisely described by global gene expression data at the protein level. We investigated whether proteome analysis based on two-dimensional gel electrophoresis and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry can identify differences in protein expression between high- and low-grade glioma tissues. Proteome profiling patterns were compared in 85 tissue samples: 52 glioblastoma multiforme, 13 anaplastic astrocytomas, 10 atrocytomas, and 10 normal brain tissues. We could completely distinguish the normal brain tissues from glioma tissues by cluster analysis based on the proteome profiling patterns. Proteome-based clustering significantly correlated with the patient survival, and we could identify a biologically distinct subset of astrocytomas with aggressive nature. Discriminant analysis extracted a set of 37 proteins differentially expressed based on histological grading. Among them, many of the proteins that were increased in high-grade gliomas were categorized as signal transduction proteins, including small G-proteins. Immunohistochemical analysis confirmed the expression of identified proteins in glioma tissues. The present study shows that proteome analysis is useful to develop a novel system for the prediction of biological aggressiveness of gliomas. The proteins identified here could be novel biomarkers for survival prediction and rational targets for antiglioma therapy.

Amino Acid Sequence↗

PubMed2004

National Heart, Lung, And Blood Institute Clinical Proteomics Working Group report.

The National Heart, Lung, and Blood Institute (NHLBI) Clinical Proteomics Working Group was charged with identifying opportunities and challenges in clinical proteomics and using these as a basis for recommendations aimed at directly improving patient care. The group included representatives of clinical and translational research, proteomic technologies, laboratory medicine, bioinformatics, and 2 of the NHLBI Proteomics Centers, which form part of a program focused on innovative technology development. This report represents the results from a one-and-a-half-day meeting on May 8 and 9, 2003. For the purposes of this report, clinical proteomics is defined as the systematic, comprehensive, large-scale identification of protein patterns ("fingerprints") of disease and the application of this knowledge to improve patient care and public health through better assessment of disease susceptibility, prevention of disease, selection of therapy for the individual, and monitoring of treatment response.

Biological Specimen Banks↗

PubMed2006

Proteomics of the heart: unraveling disease.

Heart diseases resulting in heart failure are among the leading causes of morbidity and mortality in developed countries. Underlying molecular causes of cardiac dysfunction in most heart diseases are still largely unknown but are expected to result from causal alterations in gene and protein expression. Proteomic technology now allows us to examine global alterations in protein expression in the diseased heart and can provide new insights into cellular mechanisms involved in cardiac dysfunction. The majority of proteomic investigations still use 2D gel electrophoresis (2-DE) with immobilized pH gradients to separate the proteins in a sample and combine this with mass spectrometry (MS) technologies to identify proteins. In spite of the development of novel gel-free technologies, 2-DE remains the only technique that can be routinely applied to parallel quantitative expression profiling of large sets of complex protein mixtures such as whole cell lysates. It can resolve >5000 proteins simultaneously (approximately 2000 proteins routinely) and can detect <1 ng of protein per spot. Furthermore, 2-DE delivers a map of intact proteins, which reflects changes in protein expression level, isoforms, or post-translational modifications. The use of proteomics to investigate heart disease should result in the generation of new diagnostic and therapeutic markers. In this article, we review the current status of proteomic technologies, describing the 2-DE proteomics workflow, with an overview of protein identification by MS and how these technologies are being applied to studies of human heart disease.

Developed Countries↗

PubMed2025

Proteomic Characterization of the Rhesus Macaque Lens Nucleus: Similarity to Human Lens, Age Effects on Protein Solubility, and Trends in Post-Translational Modifications.

PURPOSE: Proteomes of lens nuclei from young (4 years old) and old (15-16 years old) rhesus macaques (Macaca mulatta) were analyzed to determine similarity of the proteomic profile to that of human lenses, age-related differences in protein solubility, and association of various post-translational modifications with age and protein solubility. METHODS: Lens core proteins were separated into water-soluble and water-insoluble fractions using aqueous buffer and centrifugation. The water-insoluble fraction was solubilized using sodium dodecyl sulfate (SDS). Proteins were processed using S-trap columns, and peptide digests were analyzed using high-resolution, label-free data-dependent acquisition (DDA) proteomics. Open modification searches were performed using MSFragger to identify possible post-translational modifications (PTMs). The number of modified peptide tandem mass spectra confidently assigned to samples by age or solubility were compared to find PTMs with statistically significant count differences. RESULTS: The overall proteomic profile of rhesus macaque lenses was very similar to human lenses, consisting of 80.2% crystallins, 1.1% beaded filament proteins, and 18.7% other proteins. The crystallin fraction consisted of 27% alpha crystallins, 67.6% beta/gamma crystallins, and 5.4% taxon-specific psi crystallin. Glycolytic enzymes, beta/gamma crystallins, and a few glutathione-related enzymes were found to have age-related shifts to the water-insoluble fraction. There were significant differences in deamidation, dioxidation, carbamylation, carboxymethylation, and trioxidation based on age and/or solubility of proteins. CONCLUSIONS: These data indicate a high level of conformity between rhesus macaque and human lens proteomes, and a few key differences. We identified several age-related differences in protein solubility and PTM that may contribute to lens pathology.

Animals↗

PubMed2026

Integrated analysis of gut microbiota, serum metabolomics, and proteomics reveals novel associations with clinical symptoms in patients with cerebral infarction.

BACKGROUND: Cerebral infarction (CI) is a major cause of adult disability and mortality worldwide. Mounting evidence supports the critical role of the gut-brain axis in cerebrovascular disease progression. This study aimed to characterize the alterations in gut microbiota, serum metabolome, and serum proteome in patients with CI, and to identify multi-omics signatures associated with clinical symptoms. METHODS: A total of 20 CI patients and 20 healthy controls (HC) were enrolled. Fecal microbiota was profiled using 16 S rRNA gene high-throughput sequencing. Serum metabolomics and proteomics were analyzed using ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and data-independent acquisition (DIA) proteomics, respectively. Spearman correlation and multi-omics integration were applied to explore the associations among microbiota, metabolites, proteins, and clinical indicators. RESULTS: CI patients displayed significant gut microbiota dysbiosis, with a markedly lower gut microbiota health index (GMHI) and higher microbiota disorder index (MDI) compared with HC (P < 0.001). The genera g_norank_o_RF39 and Oxalobacter were significantly enriched in CI patients, whereas Clostridium_sensu_stricto_1 and Agathobacter were enriched in HC. Metabolomic analysis identified 445 differential metabolites, mainly involved in glycerophospholipid metabolism, phenylalanine metabolism, and caffeine metabolism. Proteomic analysis revealed 140 differentially expressed proteins linked to inflammatory responses, calcium signaling, and NF-κB signaling. Multi-omics integration showed that signature gut microbiota was strongly correlated (P < 0.005) with key serum metabolites and proteins implicated in CI pathogenesis. CONCLUSIONS: This integrated multi-omics study revealed distinct gut microbiota, serum metabolomic, and proteomic alterations in CI patients. The microbiota-metabolite-protein regulatory axes provide novel insights into the gut-brain axis in CI and may serve as potential diagnostic biomarkers or therapeutic targets.

Humans↗

PubMed2006

Proteomic analysis of human neutrophils.

Proteomics is the study of the set of proteins, or proteome, expressed by a cell under specific conditions. Proteomics methodology consists of protein extraction, protein separation, and protein identification. Currently, two-dimensional gel electrophoresis (2DE) and matrix-assisted laser-desorption ionization time of flight mass spectrometry are the most widespread methods for proteomic studies. The recent introduction of precast immobilized pH gradient gel strips, precast gradient sodium dodecyl sulfate-polyacrylamide gel electrophoresis gels, and well-designed electrophoresis equipment has made 2DE a highly reproducible and relatively simple method for protein separation. Inherent limitations of the procedure, however, require approaches in sample preparation that may be cell- or tissue-dependent. This chapter describes a methodology for proteomic analysis of human neutrophils and discusses its applications.

Electrophoresis, Gel, Two-Dimensional↗

PubMed2004

Current status of proteomic standards development.

The generation of proteomic data is becoming ever more high throughput. Both the technologies and experimental designs used to generate and analyze data are becoming increasingly complex. The need for methods by which such data can be accurately described, stored and exchanged between experimenters and data repositories has been recognized. Work by the Proteome Standards Initiative of the Human Proteome Organization has laid the foundation for the development of standards by which experimental design can be described and data exchange facilitated. The Minimum Information About a Proteomic Experiment data model describes both the scope and purpose of a proteomics experiment and encompasses the development of more specific interchange formats such as the mzData model of mass spectrometry. The eXtensible Mark-up Language-MI data interchange format, which allows exchange of molecular interaction data, has already been published and major databases within this field are supplying data downloads in this format.

Databases, Protein↗

PubMed2005

Proteomics in hematologic malignancies.

Basic science research in hematology has been determining the functions of gene products using classical approaches that typically involve studying one or a few genes at a time. Proteomics, defined as the study of protein properties on a large scale, provides tools to globally analyze malignant hematologic cells. A major challenge in cancer therapy is the identification of drugs that kill tumor cells while preserving normal cells. Differential display via proteomics enables analysis of direct as well as side-effects of drugs at a molecular level. Proteomics also allows a better understanding of cell signaling pathways involved during apoptosis in hematologic cells. Storing the information in a 2D electrophoresis database enhances the efficiency of proteome research on malignant cells. Finally, the work needed to be carried out on proteomic analysis prior to routine clinical adoption is discussed, and the necessity for multi-institutional collaborations is emphasized.

Animals↗

PubMed2005

Cell cycle: proteomics gives it a spin.

The eukaryotic cell division cycle has been studied at the molecular level for over 30 years, most fruitfully in model organisms. In the past 5 years, developments in mass spectrometry-based proteomics have been applied to the study of protein interactions and post-translational modifications involving key cell cycle regulators such as cyclin-dependent kinases and the anaphase-promoting complex, as well as effectors such as centrosomes, the kinetochore and DNA replication forks. In addition, innovations in chemical biology, functional proteomics and bioinformatics have been employed to study the cell cycle at the proteome level. This review surveys the contributions of proteomics to cell cycle research. The near future should see the application of more quantitative proteomic approaches to probe the dynamic aspects of the molecular system that underlie the cell cycle in model organisms and in human cells.

Animals↗

PubMed2006

Delineating signal transduction pathways in smooth muscle through focused proteomics.

This review will outline examples of the authors' focused proteomics approaches to studying signal transduction pathways in smooth muscle. By focusing the use of traditional proteomics techniques with hypothesis-driven selection methods, this approach efficiently addresses the identification of novel elements in a signal transduction pathway of interest. However, focused proteomics serves only as a starting point in the investigation of novel signaling proteins. While focused proteomics studies can suggest the involvement and general biochemical function of a protein in a signaling pathway, these findings must be further investigated and validated. Through the integrated use of focused proteomics with complementary approaches such as genetics, biochemistry and cell physiology, a complete and detailed mechanism of signal transduction can be determined.

Animals↗

PubMed2026

Preliminary Exploration on Melatonin-Mediated Protective Effects in Intracranial Aneurysms: Transcriptomic, Proteomic, and Metabolomic Profiling of Cerebral Vascular Tissues Combined with in vivo Animal Experiments.

BACKGROUND: Intracranial aneurysm (IA) is a life-threatening cerebrovascular disease with unclear molecular mechanisms and limited drug treatment. Our previous research has shown that melatonin (MLT) has potential protective effects in IA, but its mechanism remains unclear. The purpose of this study is to explore the pathological mechanism of IA and the therapeutic mechanism of MLT by integrating transcriptomic, proteomic and metabolomic analyses. METHODS: In this study, mouse models of IA were successfully established by combining elastase injection with angiotensin II infusion. C57BL/6 mice were divided into control, IA model, IA model+MLT, and IA model+nimodipine groups. The pathological conditions were evaluated by hematoxylin-eosin (HE) staining, TUNEL staining, and scanning electron microscopy. Transcriptomic (n=3 for each group), proteomic (n=3 for each group), and metabolomic (n=6 for each group) analyses were performed based on cerebral vascular tissue samples. The screening thresholds for differentially expressed genes and differentially expressed proteins were P <0.05 and fold change >1.5 and fold change <0.667. The screening criteria for differential metabolites were variable importance for the projection (VIP)> 1.0, fold change >1.2 and fold change <0.833, and P <0.05. RESULTS: MLT alleviated brain tissue damage, vascular endothelial damage, structural disruption, and apoptosis in IA mice. Transcriptomic, proteomic and metabolomic analyses identified numerous differential molecules. Functional annotation revealed that these molecules may be involved in biological pathways and processes such as immune inflammation, vascular remodeling, extracellular matrix remodeling, neuropeptide activity, oxidative stress and metabolic pathways, thereby regulating the occurrence and development of IA or mediating the therapeutic effects of MLT. Furthermore, transcriptomic and proteomic analyses also suggest that there may be extensive post-transcriptional, translational and post-translational regulatory events in the progression of IA and the therapeutic effects of MLT. Integrated transcriptomic and proteomic analyses suggest that Npy may be a key molecule in regulating IA progression and mediating MLT therapeutic effects, and its potential value is further supported by our immunohistochemical validation results. CONCLUSION: Multi-omics integrative analysis preliminarily revealed that the potential mechanisms of MLT may involve the regulation of inflammatory response, vascular remodeling, extracellular matrix remodeling, neuropeptide activity, oxidative stress, metabolic pathways, and post-transcriptional/translational regulation.

Animals↗

PubMed2004

Screening for disease-markers and investigating drug effects by proteome profiling: can it meet expectations?

Drugs exert their functions mainly by affecting proteins. Therefore, it seems straightforward to focus on proteins in order to investigate drug effects. Unfortunately, proteins are of very high complexity, rendering it much more difficult to screen for protein alterations as compared to gene regulation. However, the efficiency and applicability of proteome analysis has been dramatically increased recently. We are on the way to be able to comprehensively assess disease-related proteome alterations, which may become an essential source of information for knowledge-based drug design. This review will provide an overview of current techniques in proteome analysis, focusing on screening technologies for biomedical research. An outlook at the future potential of proteomics supported by modern bioinformatics will highlight why proteomics is worth the effort.

Amino Acid Sequence↗

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↗

PubMed2003

Proteomic analysis of oxidatively modified proteins in Alzheimer's disease brain: insights into neurodegeneration.

Identification of oxidized proteins in Alzheimer's disease (AD) brain is hypothesized to lead to new insights into mechanisms of neurodegeneration and synapse loss in this dementing disorder that are associated with oxidative stress. Previous studies had shown increased oxidation of proteins in AD brain, but identifying those particular proteins that were specifically oxidized using standard immunochemical methods is a daunting task when one considers how many proteins there are in brain. To address this issue, proteomics has been used to identify specifically modified proteins in AD brain. This review outlines the nature of proteomics, the proteins identified in AD brain that are specifically oxidatively modified, and provides rational consequences related to neurodegeneration and synapse loss as sequelae to loss of function, due to oxidation and consistent with the known pathological and biochemical alteration in AD brain. The use of proteomics to learn about disease mechanisms is still embryonic, but the emerging techniques of proteomics represent a promising means to elucidate mechanisms of disease at the protein level. There are limitations to proteomics, and these, too, are discussed.

Alzheimer Disease↗

PubMed2003

Proteomic technology and its biomedical applications.

Proteomics has its origins in two-dimensional gel electrophoresis (2-DE), a technique developed more than twenty years ago. 2-DE has a high-resolution capacity, and was initially used primarily for separating and characterizing proteins in complex mixtures. 2-DE remains an important tool for protein identification, but is now normally coupled with mass spectrometry (MS), a technique which has advanced considerably in recent years. The recent completion of human genome project has produced a large DNA database which can be utilized through bioinformatics, and the next challenge for scientists is to uncover the entire proteome of a particular organism. The integration of genomic and proteomic data will help to elucidate the functions of proteins in the pathogenesis of diseases and the ageing process, and could lead to the discovery of novel drug target proteins and biomarkers of diseases. This review describes recent advances in proteomic technology and discusses the potential applications of proteomics in biomedical research.

Biomedical Research↗