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PubMed2003

Differential proteome analysis: two-dimensional nano-LC/MS of E. coli proteome grown on different carbon sources.

Different sugars provided to bacteria as single sources of carbon and energy require the induction of different metabolic enzymes, transporters, and uptake systems in order to support growth and cell survival. Using a nano-high-performance liquid chromatography/mass spectrometry (nano-HPLC/MS) system we constructed comprehensive peptide maps for Escherichia coli grown with either lactose or glucose in minimal medium. Digested bacterial samples were separated in a two-dimensional manner by combining strong cation exchange (SCX) and reversed-phased (RP) chromatography. Peptides were eluted online to an iontrap MS instrument and further analyzed by tandem MS fragmentation. Bacterial proteins originating from the differing samples were analyzed by searching the Swiss Prot Database. Data are presented that show the ability to detect several hundred different proteins significantly expressed under both conditions. Several enzymes and binding proteins related to the lactose metabolism were only identified in the sample grown with this carbon source.

Carbon↗
PubMed2026

Proteomics at scale: Bottlenecks and opportunities for early-career researchers in a fast developing field.

The field of proteomics has rapidly evolved over the last five years enabled by rapid advances in instrumentation and computation. At the same time, the proteomics community is also growing. This is reflected by the increasing participation in international conferences such as those organized by the European Proteomics Association and the Human Proteome Organization. These events provide early-career researchers with unique opportunities to exchange ideas, develop collaborations, and build networks that support professional development. One such network is the Young Proteomics Investigators Club, a European initiative supported by European Proteomics Association and led by early-career researchers. In this Community-Driven project, we investigate recent trends in proteomics by screening conference abstracts and evaluating the session attendance at Human Proteome Organization Congresses and European Proteomics Association conferences. Based on these analyses, we identified five areas that, from our perspective, are shaping the current trends in proteomics: clinical proteomics, proteomics of post-translational modifications, single-cell proteomics, systems biology and multi-omics, and computational proteomics. For each area, we highlight both unique challenges and identify a common theme: a shift from exploratory studies with manageable sample numbers towards large screenings and cohorts and the generation of big data, which often comes with the lack of computational support, organizational networks, and infrastructure. In this light, we describe the unique challenges and opportunities faced by early-career researchers. We point to actionable directions for enabling reproducible and transparent proteomics as well as community-driven projects and initiatives, which are often providing training and support. SIGNIFICANCE: In this perspective, the Young Proteomics Investigators Club (YPIC) discusses advances in analytical developments and computational approaches in proteomics research. Based on empirical analysis of recent European Proteomics Association conference and Human Proteome Organization congresses contributions, we identify clinical, single-cell, post-translational and systems-level proteomics as the research areas that have gained most momentum in the last three to five years. What makes this work distinctive is that it is written by and for early-career researchers, thereby uniquely identifying where momentum, challenges, and unmet needs converge for the newest generation of proteomics researchers. Rather than cataloguing advances, we examine the widening gap between what modern proteomics can generate and what individual researchers can realistically process, validate, and interpret. We describe specific structural barriers including access to high performance computing, limited formal training in scalable data analysis, the need for unified benchmarking standards and navigating clinical collaboration frameworks. We then highlight opportunities for the field, such as community-curated benchmarks, interdisciplinary mentorship models, and shared computational infrastructure. By making these challenges explicit from an early-career researchers standpoint, we aim to inform how training, funding, and community initiatives can be shaped to support the next generation of proteomics researchers.

Proteomics↗
PubMed2002

Clinical proteomics for cancer biomarker discovery and therapeutic targeting.

As we emerge into the post-genome era, proteomics finds itself as the driving force field as we translate the nucleic acid information archive into understanding how the cell actually works and how disease processes operate. Even so, the traditionally held view of proteomics as simply cataloging and developing lists of the cellular protein repertoire of a cell are now changing, especially in the sub-discipline of clinical proteomics. The most relevant information archive to clinical applications and drug development involves the elucidation of the information flow of the cell; the "software" of protein pathway networks and circuitry. The deranged circuitry of the cell as the drug target itself as well as the effect of the drug on not just the target, but also the entire network, is what we now are striving towards. Clinical proteomics, as a new and most exciting sub-discipline of proteomics, involves the bench-to-bedside clinical application of proteomic tools. Unlike the genome, there are potentially thousands of proteomes: each cell type has its own unique proteome. Moreover, each cell type can alter its proteome depending on the unique tissue microenvironment in which it resides, giving rise to multiple permutations of a single proteome. Since there is no polymerase chain reaction equivalent to proteomics- identifying and discovering the "wiring diagram" of a human diseased cell in a biopsy specimen remains a daunting challenge. New micro-proteomic technologies are being and still need to be developed to drill down into the proteomes of clinically relevant material. Cancer, as a model disease, provides a fertile environment to study the application of proteomics at the bedside. The promise of clinical proteomics and the new technologies that are developed is that we will detect cancer earlier through discovery of biomarkers, we will discover the next generation of targets and imaging biomarkers, and we can then apply this knowledge to patient-tailored therapy.

Algorithms↗
PubMed2004

Proteomics: a primer for otologists.

OBJECTIVE: On July 9, 2003, the National Institutes of Health (NIH) released a new program announcement entitled "Proteomics in Auditory and Developmental Disease Processes." This initiative makes it clear that proteomic analysis in otology is a multi-year research priority for the NIH. The goal of this article is to describe the mechanics of modern proteomic techniques and review their applications in otology to date. DATA SOURCES: General articles from the proteomic literature were used to construct a review of modern proteomic techniques. For literature on proteomics in otology, MEDLINE and CRISP databases were searched by various topics in otology and cross-referenced with principle proteomic technologies. STUDY SELECTION: The criterion for selection was any study in otology that employs proteomic technology. CONCLUSIONS: Incredible progress has been made in proteomic technology. However, modern proteomic techniques are currently underutilized in otologic research. The NIH proteomics initiative referenced above, in combination with an understanding of the basic tools of modern proteomic science, should help motivate otologists to discover innovative ways to apply modern proteomic techniques to specific problems in otology.

Biomedical Research↗
PubMed2026

Proteomics in environmental pollution research: Advances, challenges, and future directions.

Environmental proteomics has emerged as a powerful approach for elucidating the molecular mechanisms underlying pollutant-induced biological effects. Although this field has developed rapidly, the systematic review of recent proteomics applications in environmental pollution research remains limited. This review explored the emerging roles of toxicoproteomics in biomarker discovery and mechanistic elucidation, as well as ecotoxicoproteomics in ecological risk assessment and bioremediation strategies. Here, we review the field, highlighting recent trends such as the integration of proteomics with genomics, transcriptomics, and metabolomics to provide a comprehensive view of biological responses to environmental stressors. We further discuss the growing application of artificial intelligence in improving proteomics data interpretation and accelerating biomarker discovery. In addition, recent technological advances in environmental proteomics are highlighted, including next-generation tissue microarray proteomics, nanoscale proteomics, single-cell proteomics, and spatial proteomics. Despite its potential, proteomics faces challenges, such as high operational costs, computational complexity in analysis, and technical limitations in low-abundance protein detection. We propose that the convergence of proteomics with artificial intelligence and multi-omics approaches offers promising solutions to these challenges, enhancing the practical application of proteomics in environmental monitoring and risk assessment.

Proteomics↗
PubMed2006

Epithelial proteomics in multiple organs and tissues: similarities and variations between cells, organs, and diseases.

Epithelial cells play an important role in physiological and pathophysiological situations, with organ-, tissue-, type-, and function-specific patterns. Proteome analysis has been used to study epithelial-origin diseases and identify novel prognostic, diagnostic, and therapeutic markers. The present review compares the variation of sample preparation for epithelial proteomic analysis, search similarities, and differences of epithelial proteomics between different cells, locations, and diseases. We focus on specificity of proteomic markers for epithelial-involved diseases. Proteomic alterations in epithelial cell lines were mapped to understand protein patterns, differentiation, oncogenesis, and pathogenesis of epithelial-origin diseases. Changes of proteomic patterns depend on different epithelial cell lines, challenges, and preparation. Epithelial protein profiles associated with intracellular locations and protein function. Epithelial proteomics has been greatly developed to link clinical questions, e.g., disease severity, biomarkers for disease diagnosis, and drug targets. There is an exciting and attractive start to link epithelial proteomics with histology of clinical samples. From the present review, we can find that most of disease-associated investigation of epithelial proteomics has been focused on epithelial-origin cancer. There is a significant gap of epithelial proteomics between acute and chronic organ injury, inflammation, and multiple organ dysfunction. Epithelial proteomics will provide powerful information on the relationships between biological molecules and disease mechanisms. Epithelial proteomics strategies and approaches should become more global, multidimensional, and systemic.

Biomarkers, Tumor↗
PubMed2004

Towards a comprehensive understanding of Bacillus subtilis cell physiology by physiological proteomics.

Using Bacillus subtilis as a model system for functional genomics, this review will provide insights how proteomics can be used to bring the virtual life of genes to the real life of proteins. Physiological proteomics will generate a new and broad understanding of cellular physiology because the majority of proteins synthesized in the cell can be visualized. From a physiological point of view two major proteome fractions can be distinguished: proteomes of growing cells and proteomes of nongrowing cells. In the main analytical window almost 50% of the vegetative proteome expressed in growing cells of B. subtilis were identified. This proteomic view of growing cells can be employed for analyzing the regulation of entire metabolic pathways and thus opens the chance for a comprehensive understanding of metabolism and growth processes of bacteria. Proteomics, on the other hand, is also a useful tool for analyzing the adaptational network of nongrowing cells that consists of several partially overlapping regulation groups induced by stress/starvation stimuli. Furthermore, proteomic signatures for environmental stimuli can not only be applied to predict the physiological state of cells, but also offer various industrial applications from fermentation monitoring up to the analysis of the mode of action of drugs. Even if DNA array technologies currently provide a better overview of the gene expression profile than proteome approaches, the latter address biological problems in which they can not be replaced by mRNA profiling procedures. This proteomics of the second generation is a powerful tool for analyzing global control of protein stability, the protein interaction network, protein secretion or post-translational modifications of proteins on the way towards the elucidation of the mystery of life.

Bacillus subtilis↗
PubMed2004

Global analysis of predicted proteomes: functional adaptation of physical properties.

The physical characteristics of proteins are fundamentally important in organismal function. We used the complete predicted proteomes of >100 organisms spanning the three domains of life to investigate the comparative biology and evolution of proteomes. Theoretical 2D gels were constructed with axes of protein mass and charge (pI) and converted to density estimates comparable across all types and sizes of proteome. We asked whether we could detect general patterns of proteome conservation and variation. The overall pattern of theoretical 2D gels was strongly conserved across all life forms. Nevertheless, coevolved replicons from the same organism (different chromosomes or plasmid and host chromosomes) encode proteomes more similar to each other than those from different organisms. Furthermore, there was disparity between the membrane and nonmembrane subproteomes within organisms (proteins of membrane proteomes are on the average more basic and heavier) and their variation across organisms, suggesting that membrane proteomes evolve most rapidly. Experimentally, a significant positive relationship independent of phylogeny was found between the predicted proteome and Biolog profile, a measure associated with the ecological niche. Finally, we show that, for the smallest and most alkaline proteomes, there is a negative relationship between proteome size and basicity. This relationship is not adequately explained by AT bias at the DNA sequence level. Together, these data provide evidence of functional adaptation in the properties of complete proteomes.

Animals↗
PubMed2005

Population proteomics: addressing protein diversity in humans.

In the past several years, proteomics and its subdiscipline clinical proteomics have been engaged in the discovery of the next generation protein of biomarkers. As the effort and the intensive debate it has sparked continue, it is becoming apparent that a paradigm shift is needed in proteomics in order to truly comprehend the complexity of the human proteome and assess its subtle variations among individuals. This review introduces the concept of population proteomics as a future direction in proteomics research. Population proteomics is the study of protein diversity in human populations. High-throughput, top-down mass spectrometric approaches are employed to investigate, define and understand protein diversity and modulations across and within populations. Population proteomics is a discovery-oriented endeavor with a goal of establishing the incidence of protein structural variations and quantitative regulation of these modifications. Assessing human protein variations among and within populations is viewed as a paramount undertaking that can facilitate clinical proteomics' effort in discovery and validation of protein features that can be used as markers for early diagnosis of disease, monitoring of disease progression and assessment of therapy. This review outlines the growing need for analyzing individuals' proteomes and describes the approaches that are likely to be applied in such a population proteomics endeavor.

Adult↗
PubMed2005

Proteomics in nutrition and health.

Proteomics, the comprehensive analysis of a protein complement in a cell, tissue or biological fluid at a given time, has been enabled by quantum leaps in mass spectrometric technology, which allowed identification of large, involatile biomolecules. Over the last two decades, this discipline evolved from the sole delivery of protein identities to a platform, which reveals clues to function through e.g. characterisation of protein modifications and interactions as well as through quantitative proteomics, i.e. the global comparison of protein amounts between two defined biological states. Proteomics is an integral part and key player in the family of -omic disciplines as there are genomics (gene analysis), transcriptomics (gene expression analysis) and metabolomics (metabolite profiling). Considering the complexity, dynamics and protein concentration range of any given proteome, proteomics is the most challenging -omic discipline and requires the most sophisticated analysis pipeline. Proteomics represents an established technology in the pharmaceutical industry mainly for biomarker and drug target discovery. The potential of proteomics for research in the food industry is increasingly being recognised and the employment of proteomic approaches to nutrition and health issues is now emerging. This review summarizes (i) major technological achievements in mass spectrometry and proteomics, (ii) deliverables of proteomics in the context of nutrition and health, and (iii) applications of proteomics, and -- if appropriate -- transcriptomics to the research fields of digestive health, obesity and diabetes, immunity and allergy, probiotics, milk, and food preference.

Complex Mixtures↗
PubMed2003

The human proteome organization (HUPO) and environmental health.

The Human Proteome Organization, or HUPO, was formed to promote research and large-scale analysis of the human proteome. By consolidating national proteome organizations into an international body, HUPO will coordinate international initiatives, biological resources, protocols, standards and data for studying the human proteome. HUPO has identified five key areas to advance study of the human proteome, specifically in bioinformatics, new technologies, the plasma proteome, cell models, and a public antibody initiative. Consideration of three major issue areas may help develop HUPO's strategy for human proteome study. First is the need to distinguish the value of high throughput platforms from discovery platforms in proteomics. Second is the importance for international planning on integrating both transcriptome and proteome data and databases. Last is that effects of the environment from chemical, physical, and biological exposures alter the expression and structure of the proteome, which become manifest in long-term adverse health effects and disease. Environmental health research stands to greatly benefit from the shared resources, data, and vision of the HUPO organization as a valuable resource in exploiting knowledge of the human proteome toward improving public health.

Computational Biology↗
PubMed2026

Cardiac mitochondrial proteome of lean, healthy Ossabaw minipigs with predisposition to metabolic syndrome versus that of Göttingen minipigs.

Ossabaw minipigs differ from other (mini)pig strains by their genetic predisposition to develop full metabolic syndrome and their nonresponsiveness to cardioprotective interventions, even before developing the diseased phenotype. Previous DNA sequencing data revealed differences in a cluster of mitochondrial protein-coding genes between Ossabaw and Göttingen minipigs-a large animal model without such a genetic predisposition and a responsiveness to cardioprotection. Alterations in mitochondrial protein composition affect mitochondrial function, and mitochondria play a crucial role in the development of metabolic syndrome and for cardioprotection. Therefore, we aimed to compare the cardiac mitochondrial proteome between lean Ossabaw minipigs with a healthy phenotype and Göttingen minipigs to gain initial insights into potential differences in mitochondrial protein composition and function. Cardiac mitochondria (left ventricular tissue) of both minipig strains (male/female pigs) were isolated, and the proteome was analyzed by liquid chromatography-tandem mass spectrometry. An unbiased, nonhypothesis-driven proteome analysis identified 97% overlap in the proteome. Among the 3% of differentially expressed proteins, 19 were related to mitochondrial metabolism, 8 to transcription and translation, 3 to small molecule transport, 2 to oxidative phosphorylation, and 1 to dynamics and surveillance. These small differences in protein composition were associated with an altered mitochondrial energy turnover-ATP production was reduced by 49% in Ossabaw compared with Göttingen minipig mitochondria. This proteome analysis provides a broader basis to understand how genetic alterations result in changes of the mitochondrial proteome and function, which might be relevant for the development and progression of metabolic syndrome and/or the primordial nonresponsiveness to cardioprotection in Ossabaw minipigs.NEW & NOTEWORTHY Our comprehensive cardiac mitochondrial proteome of Ossabaw and Göttingen minipigs is a valuable resource for cardiac biomedical research. Moreover, our proteome analysis provides a broader basis for understanding how genetic alterations result in changes of the mitochondrial proteome and support a mechanistic link between subtle, strain-specific mitochondrial proteomic signatures and altered mitochondrial energy turnover. These changes may be relevant for the development and progression of metabolic syndrome and/or primordial nonresponsiveness to cardioprotection in Ossabaw minipigs.

Animals↗
PubMed2026

Systemic Proteome Profiling to Differentiate Primary Glomerular Diseases.

KEY POINTS: Plasma proteome profiling identified distinct signatures across biopsy-proven primary glomerular disease subtypes. An elastic net model using 93 proteins classified primary glomerular disease subtypes and controls, with external validation. Integrating proteomics with machine learning yields biologically interpretable insights in primary glomerular diseases. BACKGROUND: Primary GN is a heterogeneous group of kidney disorders where understanding of their pathophysiology remains incomplete. Despite the diagnostic potential of high-throughput proteomics, constrained proteomic depth and a reliance on binary comparisons have left the feasibility of using systemic signatures to differentiate multiple GN subtypes largely unexplored. METHODS: To identify protein signatures that noninvasively differentiate major primary glomerular disease subtypes and provide mechanistic insights, we performed large-scale systemic proteome profiling of 5416 plasma proteins via Olink Explore HT in a discovery cohort ( n =147) and an external validation cohort ( n =85) of Korean participants (mean age, 41±13 years; 46% female). The study population included patients with four GN subtypes-focal segmental glomerulosclerosis, IgA nephropathy, minimal change disease, and membranous nephropathy-alongside healthy controls. We developed a machine learning (ML) model using logistic regression with elastic net regularization to classify disease groups based on proteomic profiles and evaluated its performance in the independent validation cohort. RESULTS: Plasma proteome profiles were distinct among disease subtypes, emerging as a significant source of data variation independent of conventional markers such as eGFR or proteinuria levels. The ML model performed robustly in both the discovery and validation cohorts, achieving an area under the receiver operating characteristic curve >0.8 for differentiating minimal change disease, membranous nephropathy, and IgA nephropathy. The model, even without clinical information, correctly identified 93% of minimal change disease cases (14 of 15) and 63% of IgA nephropathy cases (20 of 32), but its performance was limited for focal segmental glomerulosclerosis, with only 21% of cases (three of 14) correctly classified. Functional analysis of key proteins highlighted distinct biologic pathways, such as hemostasis in minimal change disease. CONCLUSIONS: We identified distinct systemic proteome signatures for primary glomerular diseases, where disease subtype served as a major determinant of proteomic variance alongside conventional clinical markers. ML models demonstrated robust discriminatory performance for minimal change disease, membranous nephropathy, and IgA nephropathy, underscoring the potential for proteome-based classification.

Humans↗
PubMed2006

Rejuvenating rice proteomics: facts, challenges, and visions.

Proteomics is progressing at an unprecedented pace, as can be exemplified by the progress in model organisms such as yeast, bacteria, and mammals. Proteomics research in plants, however, has not progressed at the same pace. Unscrambling of the genome sequences of the dicotyledoneous Arabidopsis thaliana (L.) and monocotyledoneous rice (Oryza sativa L.) plant species, respectively, has made them accessible reference organisms to study plant proteomics. Study of these two reference plants is expected to unravel the mystery of plant biology. Rice, a critically important food crop on the earth, has been termed a "cornerstone" and the "Rosetta stone" for functional genomics of cereal crops. Here, we look at the progress in unraveling rice proteomes and present the facts, challenges, and vision. The text is divided into two major parts: the first part presents the facts and the second part discusses the challenges and vision. The facts include the technology and its use in developing proteomes, which have been critically and constructively reviewed. The challenges and vision deal with the establishment of technologies to exhaustively investigate the protein components of a proteome, to generate high-resolution gel-based reference maps, and to give rice proteomics a functional dimension by studying PTMs and isolation of multiprotein complexes. Finally, we direct a vision on rice proteomics. This is our third review in series on rice proteomics, which aims to stimulate an objective discussion among rice researchers and to understand the necessity and impact of unraveling rice proteomes to their full potential.

Arabidopsis↗
PubMed2006

Plant proteome analysis: a 2004-2006 update.

Since the appearance of the review entitled "Plant Proteome Analysis" in Proteomics in February 2004 (Cánovas, F. M., Dumas-Gaudot, E., Recorbert, G., Jorrín, J. et al., Proteomics 2004, 4, 285-298), about 200 original articles focusing on plant proteomics have been published. Although this represents less than 1% of the global proteomics output during this period, it nevertheless reflects an increase in activity over the period 1999-2004. These papers concern the proteome of at least 35 plant species but have concentrated mainly on thale cress (Arabidopsis thaliana) and rice (Oryza sativa). The scientific objectives have ranged from a proteomic analysis of organs, tissues, cell suspensions, or subcellular fractions to the study of plant development and response to various stresses. A number of contributions have covered PTMs and protein interactions. The dominant analytical platform has been 2-DE coupled to MS, but "second generation" techniques such as DIGE, multidimensional protein identification technology, isotope-coded affinity tags, and stable isotope labeling by amino acids in cell culture have begun to make an impact. This review aims to provide an update of the contribution of proteomics to plant biology during the period 2004-2006, and is divided into six sections: introduction, subcellular proteomes, plant development, responses to biotic and abiotic stresses, PTMs, and protein interactions. The conclusions summarize a view of the major pitfalls and challenges of plant proteomics.

Arabidopsis↗
PubMed2005

High throughput proteome screening for biomarker detection.

Mass spectrometry-based quantitative proteomics has become an important component of biological and clinical research. Current methods, while highly developed and powerful, are falling short of their goal of routinely analyzing whole proteomes mainly because the wealth of proteomic information accumulated from prior studies is not used for the planning or interpretation of present experiments. The consequence of this situation is that in every proteomic experiment the proteome is rediscovered. In this report we describe an approach for quantitative proteomics that builds on the extensive prior knowledge of proteomes and a platform for the implementation of the method. The method is based on the selection and chemical synthesis of isotopically labeled reference peptides that uniquely identify a particular protein and the addition of a panel of such peptides to the sample mixture consisting of tryptic peptides from the proteome in question. The platform consists of a peptide separation module for the generation of ordered peptide arrays from the combined peptide sample on the sample plate of a MALDI mass spectrometer, a high throughput MALDI-TOF/TOF mass spectrometer, and a suite of software tools for the selective analysis of the targeted peptides and the interpretation of the results. Applying the method to the analysis of the human blood serum proteome we demonstrate the feasibility of using mass spectrometry-based proteomics as a high throughput screening technology for the detection and quantification of targeted proteins in a complex system.

Automation↗
PubMed2006

MAPU: Max-Planck Unified database of organellar, cellular, tissue and body fluid proteomes.

Mass spectrometry (MS)-based proteomics has become a powerful technology to map the protein composition of organelles, cell types and tissues. In our department, a large-scale effort to map these proteomes is complemented by the Max-Planck Unified (MAPU) proteome database. MAPU contains several body fluid proteomes; including plasma, urine, and cerebrospinal fluid. Cell lines have been mapped to a depth of several thousand proteins and the red blood cell proteome has also been analyzed in depth. The liver proteome is represented with 3200 proteins. By employing high resolution MS and stringent validation criteria, false positive identification rates in MAPU are lower than 1:1000. Thus MAPU datasets can serve as reference proteomes in biomarker discovery. MAPU contains the peptides identifying each protein, measured masses, scores and intensities and is freely available at http://www.mapuproteome.com using a clickable interface of cell or body parts. Proteome data can be queried across proteomes by protein name, accession number, sequence similarity, peptide sequence and annotation information. More than 4500 mouse and 2500 human proteins have already been identified in at least one proteome. Basic annotation information and links to other public databases are provided in MAPU and we plan to add further analysis tools.

Animals↗