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PubMedDate not supplied

Building integrated approaches for the proteomics of complex, dynamic systems: NIH programs in technology and infrastructure development.

Proteomics technology and methods remain inadequate. Technological constraints contribute to an artificially static view of complex biological systems and a barrier between quantitative and interaction studies. Several NIH programs combine proteomics technology development with research on challenging biological problems to drive progress. A new initiative of the NIH Roadmap focuses on characterization of dynamic systems. The success of these programs will be judged by their impact on relevant biological problems.

Biomedical Research↗

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↗

PubMed2000

The integration of SPR biosensors with mass spectrometry: possible applications for proteome analysis.

The successful integration of biospecific interaction analysis based on surface plasmon resonance and mass spectrometry produces a powerful technique that couples the benefits of sensitive affinity capture and characterization of binding events with the ability to characterize interacting molecules. A variety of biosensors has been used to capture proteins and peptides biospecifically on sensor surfaces, with subsequent analysis using mass spectrometry. Applying this type of analysis to proteomic studies could lead to ligand and protein-complex identification, and might provide clues leading to the identification of pathways.

Amino Acid Sequence↗

PubMed2004

Integrative analysis of the mitochondrial proteome in yeast.

In this study yeast mitochondria were used as a model system to apply, evaluate, and integrate different genomic approaches to define the proteins of an organelle. Liquid chromatography mass spectrometry applied to purified mitochondria identified 546 proteins. By expression analysis and comparison to other proteome studies, we demonstrate that the proteomic approach identifies primarily highly abundant proteins. By expanding our evaluation to other types of genomic approaches, including systematic deletion phenotype screening, expression profiling, subcellular localization studies, protein interaction analyses, and computational predictions, we show that an integration of approaches moves beyond the limitations of any single approach. We report the success of each approach by benchmarking it against a reference set of known mitochondrial proteins, and predict approximately 700 proteins associated with the mitochondrial organelle from the integration of 22 datasets. We show that a combination of complementary approaches like deletion phenotype screening and mass spectrometry can identify over 75% of the known mitochondrial proteome. These findings have implications for choosing optimal genome-wide approaches for the study of other cellular systems, including organelles and pathways in various species. Furthermore, our systematic identification of genes involved in mitochondrial function and biogenesis in yeast expands the candidate genes available for mapping Mendelian and complex mitochondrial disorders in humans.

Chemical Fractionation↗

PubMed2001

Integrating molecular medicine with functional proteomics: realities and expectations.

We analyze key proteomic issues and cutting-edge technologies that will spearhead inroads into functional interpretations of human diseases and their therapeutic rectification, following the availability of the predicted human proteome. We contrast the distinctions between high quality data that are low throughput, (e.g., 3-D proteomic reconstructions in embryogenic and nervous system contexts, and multigenerational transgenic studies), versus automated data harvesting that is more distant from human disease phenotypes and currently fulfills a diagnostic role, (e.g., molecular portraits of human diseases via transcriptomic analyses). We examine the extent to which these approaches impinge upon a realistic understanding of human diseases, namely how close they come to revealing the causal events involved in the initiation of disease. While tissue sources from human embryogenesis, foetal development and the brain remain the absolute priority, the pragmatic approaches utilize judicious data integration from selected proteomic studies of model organisms. The role of genome-wide disease-related screens, "humanized" transgenic analyses, multigenerational gene interference methods, and analyses of post-translational modifications in epigenetic contexts from Drosophila will be crucial, since these avenues are far too slow and transgenically cumbersome in mammals. Finally, the implementation of multi compartment electrolyzers (MCE) and multi photon detection (MPD) systems will be pivotal for the proteomic profiling of human tissue samples.

Animals↗

PubMed2005

Interpretation of shotgun proteomic data: the protein inference problem.

The shotgun proteomic strategy based on digesting proteins into peptides and sequencing them using tandem mass spectrometry and automated database searching has become the method of choice for identifying proteins in most large scale studies. However, the peptide-centric nature of shotgun proteomics complicates the analysis and biological interpretation of the data especially in the case of higher eukaryote organisms. The same peptide sequence can be present in multiple different proteins or protein isoforms. Such shared peptides therefore can lead to ambiguities in determining the identities of sample proteins. In this article we illustrate the difficulties of interpreting shotgun proteomic data and discuss the need for common nomenclature and transparent informatic approaches. We also discuss related issues such as the state of protein sequence databases and their role in shotgun proteomic analysis, interpretation of relative peptide quantification data in the presence of multiple protein isoforms, the integration of proteomic and transcriptional data, and the development of a computational infrastructure for the integration of multiple diverse datasets.

Amino Acid Sequence↗

PubMedDate not supplied

Integrating 'top-down" and "bottom-up" mass spectrometric approaches for proteomic analysis of Shewanella oneidensis.

Here we present a comprehensive method for proteome analysis that integrates both intact protein measurement ("top-down") and proteolytic fragment characterization ("bottom-up") mass spectrometric approaches, capitalizing on the unique capabilities of each method. This integrated approach was applied in a preliminary proteomic analysis of Shewanella oneidensis, a metal-reducing microbe of potential importance to the field of bioremediation. Cellular lysates were examined directly by the "bottom-up" approach as well as fractionated via anion-exchange liquid chromatography for integrated studies. A portion of each fraction was proteolytically digested, with the resulting peptides characterized by on-line liquid chromatography/tandem mass spectrometry. The remaining portion of each fraction containing the intact proteins was examined by high-resolution Fourier transform mass spectrometry. This "top-down" technique provided direct measurement of the molecular masses for the intact proteins and thereby enabled confirmation of post-translational modifications, signal peptides, and gene start sites of proteins detected in the "bottom-up" experiments. A total of 868 proteins from virtually every functional class, including hypotheticals, were identified from this organism.

Amino Acid Sequence↗

PubMed2005

A comprehensive survey of the Plasmodium life cycle by genomic, transcriptomic, and proteomic analyses.

Plasmodium berghei and Plasmodium chabaudi are widely used model malaria species. Comparison of their genomes, integrated with proteomic and microarray data, with the genomes of Plasmodium falciparum and Plasmodium yoelii revealed a conserved core of 4500 Plasmodium genes in the central regions of the 14 chromosomes and highlighted genes evolving rapidly because of stage-specific selective pressures. Four strategies for gene expression are apparent during the parasites' life cycle: (i) housekeeping; (ii) host-related; (iii) strategy-specific related to invasion, asexual replication, and sexual development; and (iv) stage-specific. We observed posttranscriptional gene silencing through translational repression of messenger RNA during sexual development, and a 47-base 3' untranslated region motif is implicated in this process.

3' Untranslated Regions↗

PubMed2006

A mammalian organelle map by protein correlation profiling.

Protein localization to membrane-enclosed organelles is a central feature of cellular organization. Using protein correlation profiling, we have mapped 1,404 proteins to ten subcellular locations in mouse liver, and these correspond with enzymatic assays, marker protein profiles, and confocal microscopy. These localizations allowed assessment of the specificity in published organellar proteomic inventories and demonstrate multiple locations for 39% of all organellar proteins. Integration of proteomic and genomic data enabled us to identify networks of coexpressed genes, cis-regulatory motifs, and putative transcriptional regulators involved in organelle biogenesis. Our analysis ties biochemistry, cell biology, and genomics into a common framework for organelle analysis.

Animals↗

PubMed2025

Multi-omic integration with human dorsal root ganglia proteomics highlights TNFα signalling as a relevant sexually dimorphic pathway.

The peripheral nervous system (PNS) plays a critical role in pathological conditions, including chronic pain disorders, that manifest differently in men and women. To investigate this sexual dimorphism at the molecular level, we integrated quantitative proteomic profiling of human dorsal root ganglia (hDRG) and peripheral nerve tissue into the expanding omics framework of the PNS. Using data-independent acquisition (DIA) mass spectrometry, we characterized a comprehensive proteomic profile, validating tissue-specific differences between the hDRG and peripheral nerve. Through multi-omic analyses and in vitro functional assays, we identified sex-specific molecular differences, with TNFα signalling emerging as a key sexually dimorphic pathway with higher prominence in men. Genetic evidence from genome-wide association studies further supports the functional relevance of TNFα signalling in the periphery, while clinical trial data and meta-analyses indicate a sex-dependent response to TNFα inhibitors. Collectively, these findings underscore a functionally sexual dimorphism in the PNS, with direct implications for sensory and pain-related clinical translation.

Humans↗

PubMed2002

Application of genomics and proteomics for study of the integrated response to zinc exposure in a non-model fish species, the rainbow trout.

The advent of DNA array technology and proteomics has revolutionised biology by allowing global analysis of cellular events. So far, the benefits from these new techniques have primarily been realised for well-characterised species. These organisms are rarely the most relevant for environmental biology and ecotoxicology. Thus, there is a need to explore new ways to exploit transcriptomics and proteomics for non-model species. In the present study, rainbow trout (Oncorhynchus mykiss) were exposed to a sublethal concentration of waterborne zinc for up to 6 days. The response in gill tissue was investigated by differential screening of a heterologous cDNA array and by protein profiling using Surface Enhanced Laser Desorption/Ionisation (SELDI). The cDNA array, which was a high-density spotted library of cDNA from Fugu rubripes gill, revealed differentially expressed genes related to energy production, protein synthesis, paracellular integrity, and inflammatory response. SELDI analysis yielded seven proteins that were consistently present only in zinc-exposed gills, and four proteins unique to gills from control fish. A further 11 proteins were differentially regulated. Identification of these proteins by bioinformatics proved difficult in spite of detailed information on molecular mass, charge and zinc-binding affinity. It is concluded that these approaches are viable to non-model species although both have clear limitations.

Animals↗

PubMed2006

Bringing metabolic networks to life: integration of kinetic, metabolic, and proteomic data.

BACKGROUND: Translating a known metabolic network into a dynamic model requires reasonable guesses of all enzyme parameters. In Bayesian parameter estimation, model parameters are described by a posterior probability distribution, which scores the potential parameter sets, showing how well each of them agrees with the data and with the prior assumptions made. RESULTS: We compute posterior distributions of kinetic parameters within a Bayesian framework, based on integration of kinetic, thermodynamic, metabolic, and proteomic data. The structure of the metabolic system (i.e., stoichiometries and enzyme regulation) needs to be known, and the reactions are modelled by convenience kinetics with thermodynamically independent parameters. The parameter posterior is computed in two separate steps: a first posterior summarises the available data on enzyme kinetic parameters; an improved second posterior is obtained by integrating metabolic fluxes, concentrations, and enzyme concentrations for one or more steady states. The data can be heterogeneous, incomplete, and uncertain, and the posterior is approximated by a multivariate log-normal distribution. We apply the method to a model of the threonine synthesis pathway: the integration of metabolic data has little effect on the marginal posterior distributions of individual model parameters. Nevertheless, it leads to strong correlations between the parameters in the joint posterior distribution, which greatly improve the model predictions by the following Monte-Carlo simulations. CONCLUSION: We present a standardised method to translate metabolic networks into dynamic models. To determine the model parameters, evidence from various experimental data is combined and weighted using Bayesian parameter estimation. The resulting posterior parameter distribution describes a statistical ensemble of parameter sets; the parameter variances and correlations can account for missing knowledge, measurement uncertainties, or biological variability. The posterior distribution can be used to sample model instances and to obtain probabilistic statements about the model's dynamic behaviour.

Bayes Theorem↗

PubMed2005

Toxicogenomics concepts and applications to study hepatic effects of food additives and chemicals.

Transcriptomics, proteomics and metabolomics are genomics technologies with great potential in toxicological sciences. Toxicogenomics involves the integration of conventional toxicological examinations with gene, protein or metabolite expression profiles. An overview together with selected examples of the possibilities of genomics in toxicology is given. The expectations raised by toxicogenomics are earlier and more sensitive detection of toxicity. Furthermore, toxicogenomics will provide a better understanding of the mechanism of toxicity and may facilitate the prediction of toxicity of unknown compounds. Mechanism-based markers of toxicity can be discovered and improved interspecies and in vitro-in vivo extrapolations will drive model developments in toxicology. Toxicological assessment of chemical mixtures will benefit from the new molecular biological tools. In our laboratory, toxicogenomics is predominantly applied for elucidation of mechanisms of action and discovery of novel pathway-supported mechanism-based markers of liver toxicity. In addition, we aim to integrate transcriptome, proteome and metabolome data, supported by bioinformatics to develop a systems biology approach for toxicology. Transcriptomics and proteomics studies on bromobenzene-mediated hepatotoxicity in the rat are discussed. Finally, an example is shown in which gene expression profiling together with conventional biochemistry led to the discovery of novel markers for the hepatic effects of the food additives butylated hydroxytoluene, curcumin, propyl gallate and thiabendazole.

Animals↗

PubMed2005

Proteomics in prostate cancer.

PURPOSE OF REVIEW: State-of-the-art proteomics technologies are currently being assessed for utility in the study of prostatic malignancy. This review aims to provide background information on the current proteomics techniques employed in prostate cancer research, recent reports showing the potential application of proteomics in urological practice, and the future direction of proteomics in prostate cancer research and management. RECENT FINDINGS: Proteomic profiling of serum as a diagnostic tool and a platform for biomarker discovery in prostate cancer continues to draw favorable attention as well as close scrutiny as technological enhancements and multi-center study results are reported. In-vitro studies on prostate cell lines provide positive proof-of-principle results. The application of proteomics to query prostate tissue specimens yields novel prostate cancer biomarkers requiring further validation. The integration of proteomics with immunology also yields promising findings that may translate into clinically relevant biological assays. SUMMARY: The study of proteomics is an emerging research field, and current studies continue to display potential future usage in prostate cancer management. Succeeding scientific investigations will probably yield new diagnostic and prognostic tools for prostate cancer, provide insights into its underlying biology, and contribute to the development of novel treatment strategies.

Gene Expression↗

PubMed2003

High-content proteomics: fluorescence multiplexing using an integrated, high-sensitivity, multiwavelength charge-coupled device imaging system.

The detection of proteins in 2-D gels and their subsequent identification by MS is still the "gold standard" in proteomics. Fluorescent detection has increasingly replaced colorimetric and radiometric detection on gels and blots. The reasons for this are multiple and varied and include higher sensitivity, better quantitation, increased dynamic range, speed, safety and ease of use. Unlike other methods, fluorescent protein detection is also typically very consistent in response from protein to protein and in many cases is compatible with MS methods for protein identification. The superior sensitivity and benefits achieved by fluorescent techniques have spurred the development of instrumentation capable of delivering precise, sensitive, high-resolution image acquisition over a wide variety of excitation and emission wavelengths. This report focuses on applications using the highly sensitive, charge-coupled device based ProXPRESS multilabel imager, readily configurable for image acquisition over a wide variety of wavelengths (380-700 nm and ultraviolet (UV)) using xenon lamp or UV excitation. The ability to simultaneously detect enzyme activities or protein modifications with different color fluorescent probes in addition to total protein amounts (multiplexing) allows the further mining of proteomic data content from a single set of protein samples. To this end, the development of instrumentation that enables a multiplexing strategy will become central to in-depth proteomic studies. The ProXPRESS maximizes the efficiency of experimental strategies that require flexibility and multicolor fluorescence detection.

Electrophoresis, Gel, Two-Dimensional↗

PubMed2002

Complementary profiling of gene expression at the transcriptome and proteome levels in Saccharomyces cerevisiae.

Using an integrated genomic and proteomic approach, we have investigated the effects of carbon source perturbation on steady-state gene expression in the yeast Saccharomyces cerevisiae growing on either galactose or ethanol. For many genes, significant differences between the abundance ratio of the messenger RNA transcript and the corresponding protein product were observed. Insights into the perturbative effects on genes involved in respiration, energy generation, and protein synthesis were obtained that would not have been apparent from measurements made at either the messenger RNA or protein level alone, illustrating the power of integrating different types of data obtained from the same sample for the comprehensive characterization of biological systems and processes.

Databases as Topic↗

PubMed2026

Proteomic profiling of bone for the estimation of post-mortem interval and post-mortem submersion interval: a systematic review.

Accurate estimation of the Post-Mortem Interval (PMI) and Post-Mortem Submersion Interval (PMSI) remains a persistent challenge in forensic science, especially when traditional morphological and entomological methods fail due to advanced decomposition or in aquatic environments. Proteomic profiling of bone tissues has recently emerged as a promising approach, leveraging the predictable degradation patterns of bone proteins to estimate time since death more reliably. This systematic review, conducted in accordance with PRISMA guidelines, analyzed 24 peer-reviewed studies focusing on the application of proteomic techniques to bone tissue for PMI and PMSI estimation. The included studies were evaluated based on sample type, analytical techniques used, identified biomarkers, environmental conditions assessed, and the overall reliability and reproducibility of the findings. The review found that specific bone proteins, particularly collagen, osteocalcin, fetuin-A, etc. exhibited consistent degradation patterns that correlated strongly with elapsed post-mortem time. Cortical bone was identified as a more stable and informative matrix compared to trabecular bone. Mass spectrometry, especially LC-MS/MS, emerged as the predominant analytical technique due to its high sensitivity and accuracy in detecting low-abundance proteins over extended PMIs and PMSIs. However, protein degradation rates were significantly influenced by environmental variables such as temperature, humidity, soil pH, and microbial activity. This review also emphasizes the transformative role of bone proteomics in advancing forensic science while identifying key gaps that must be addressed to achieve global standardization and practical implementation in diverse forensic contexts. The integration of proteomics with other emerging technologies, such as machine learning algorithms and computational modeling, may further enhance the precision of PMI and PMSI estimation in future applications.

Postmortem Changes↗

PubMed2026

Dynamic lysine acetylation and succinylation of platelet proteins regulates platelet storage lesion: mechanistic insights from multi-omics.

OBJECTIVES: Platelet storage lesion (PSL) severely impairs platelet function during storage, presenting a major hurdle in transfusion medicine; however, the dynamic interplay between global proteomic changes and post-translational modifications (PTMs) underlying these functional deteriorations remains insufficiently characterized. Here, we report the first comprehensive multi-omics analysis integrating global proteomics, acetylomics, and succinylomics to dissect the molecular dynamics during platelet storage. METHODS: We performed quantification of global proteomics, acetylome and succinylome based on TMT-labeled LC-MS/MS analysis, combined with antibody-affinity enrichment and purification. Dynamic molecular changes and functional transformation of platelet were also characterized under proper conditions stored for 1, 3, 5, 7 days, respectively. RESULTS: We systematically characterized 3,609 proteins, 1,308 acetylation sites, and 1,947 succinylation sites across multiple storage time points (D1, D3, D5, D7). We distinct temporal patterns of post-translational modifications, with succinylation showing more extensive coverage than acetylation in platelets. Pathway enrichment analysis revealed extensive metabolic reprogramming involving complement activation, energy metabolism, and cellular detoxification processes. The identification of specific motif patterns provided mechanistic insights into the functional specificity of these modifications. Random forest machine learning identified 20 core regulatory proteins representing critical nodes in PSL development. Furthermore, we employed real - time quantitative polymerase chain reaction (RT - QPCR) to measure the expression levels of key genes related to platelet function and PTM - associated pathways. CONCLUSION: By mapping the interplay between proteomic abundance shifts and PTM dynamics, this study provides a multidimensional understanding of PSL, establishing a foundational framework for optimizing storage protocols and enhancing transfusion safety.

Blood Platelets↗