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At least 343 records · Page 19Linked to original sources
PubMed2003

A comparative proteomics resource: proteins of Arabidopsis thaliana.

Using an integrative genome annotation pipeline (iGAP) for proteome-wide protein structure and functional domain assignment, we analyzed all the proteins of Arabidopsis thaliana. Three-dimensional structures at the level of the domain are assigned by fold recognition and threading based on a novel fold library that extends common domain classifications. iGAP is being applied to proteins from all available proteomes as part of a comparative proteomics resource. The database is accessible from the web.

Arabidopsis↗
PubMed2006

Challenges and opportunities in proteomics data analysis.

Accurate, consistent, and transparent data processing and analysis are integral and critical parts of proteomics workflows in general and for biomarker discovery in particular. Definition of common standards for data representation and analysis and the creation of data repositories are essential to compare, exchange, and share data within the community. Current issues in data processing, analysis, and validation are discussed together with opportunities for improving the process in the future and for defining alternative workflows.

Databases, Protein↗
PubMed2026

Proteomics as a theranostic compass in BCR::ABL1-negative myeloproliferative neoplasms: Integrating biomarker discovery with therapeutic stratification.

Classic BCR::ABL1-negative myeloproliferative neoplasms (MPNs)-polycythaemia vera, essential thrombocythaemia, and primary myelofibrosis-are clonal haematopoietic stem cell disorders with marked heterogeneity in clinical phenotype, disease trajectory, and therapeutic response. Genomic stratification by driver and cooperating mutations only partially accounts for this variability, leaving gaps in predicting thrombotic risk, fibrotic progression, leukaemic transformation, and treatment benefit. Proteomics bridges this gap by providing function-proximal readouts of protein abundance, post-translational modifications, pathway activity, and intercellular signalling that genomics and transcriptomics cannot capture, positioning it as a theranostic platform in which the same molecular readouts simultaneously inform diagnostic stratification and therapeutic decision-making. We propose a five-stage translational framework spanning from discovery-scale mass spectrometry and affinity-based plasma profiling to targeted validation, multicentre standardisation, and machine learning-integrated clinical panels. Proteomic evidence is synthesised across the following four disease axes: clonal fitness in haematopoietic stem and progenitor cells; bone marrow microenvironmental remodelling and fibrosis; chronic inflammation and thrombosis; and leukaemic transformation. We further describe how phosphoproteomics reveals resistance mechanisms to JAK inhibitors, including AXL-MAPK bypass and PP2A-autophagy-mediated tolerance, and how protein-level biomarkers (BCL2-BCL-XL, RAS-ERK, CAMK2G, and ROCK1/2) can guide individualised therapeutic selection. Affinity-based platforms (Olink PEA and SomaScan) and spatially resolved technologies (CODEX and single-cell proteomics) complement discovery proteomics. At present, however, this evidence base is constrained by small and heterogeneous cohorts, limited cross-platform reproducibility, and a scarcity of independent external validation for candidate protein panels. Realising this vision will require multicentre standardisation, analytically validated panel assays, and prospective clinical studies that translate molecular findings into decision-grade tools for patients with MPNs.

Humans↗
PubMed2026

Large-scale multi-omics enhance risk prediction for type 2 diabetes.

BACKGROUND: Polygenic risk scores (PRS), metabolomics, and proteomics have each shown promise in improving type 2 diabetes risk prediction, but their combined utility beyond established clinical models remains unclear. We aimed to evaluate whether integrating multi-omics biomarkers enhances 10-year type 2 diabetes risk prediction beyond single-omics extensions and the clinical Cambridge Diabetes Risk Score (CDRS), which includes HbA1c measurements. METHODS: We analysed data from 42,840 UK Biobank participants without diagnosed diabetes at baseline. The study population was split into a derivation set (Phase 1 metabolomics release, N&#x2009;=&#x2009;23,108) to fit models and an independent validation set (Phase 2 release, N&#x2009;=&#x2009;19,732) to evaluate performance. Data for a PRS for type 2 diabetes, 11 metabolites, and 15 proteins were added to the CDRS to develop multi-omics prediction models. Model performance was evaluated using Harrell's C-index and the net reclassification index (NRI). RESULTS: During 10 years of follow-up, 1090 participants developed incident type 2 diabetes. Among individual omics layers, proteomics contributed the greatest improvement in predictive performance, increasing the C-index from 0.862 (clinical CDRS) to 0.884 (&#x394;C-index; + 0.022; P&#x2009;<&#x2009;0.001), with a continuous NRI of 42.0%. The full multi-omics model further significantly increased the C-index compared to a model combining the clinical CDRS with proteomics data (C-index, 0.891; &#x394;C-index; + 0.007; P&#x2009;<&#x2009;0.001). CONCLUSION: Integrating proteomics, metabolomics, and a diabetes-PRS into a clinical model substantially improves type 2 diabetes risk prediction beyond single-omics extensions. Several of the selected proteins and metabolites are on cardiovascular disease pathways, highlighting the link between diabetes and cardiovascular risk. However, the C-index difference between the proteomics extended and full multi-omics extended models is small, and the clinical models extended with proteomics data would be easier to translate into routine care because it needs only the measurement of 15 proteins. External validation and cost-effectiveness analyses are needed to support clinical adoption.

Humans↗
PubMed2003

The pre-ribosomal network.

Recent achievements in yeast functional proteomics have significantly advanced our knowledge about ribosome biogenesis. Here, we present a program developed to integrate data from various proteome analyses with cell biological data on components present in the ribosome producing factories. This program allows users to attribute factors to certain complexes and to specific steps of ribosome biogenesis. Thus, it helps to gain novel insights into the complex network involved in maturation of ribosomal subunits. The database can be accessed at the URL http://www.pre-ribosome.de.

Biomarkers↗
PubMed2002

Annotating the human proteome: the Human Proteome Survey Database (HumanPSD) and an in-depth target database for G protein-coupled receptors (GPCR-PD) from Incyte Genomics.

The Proteome Division of Incyte Genomics has released new volumes to the BioKnowledge Library to add human, mouse and rat protein information to its rich collection of model organism Proteome Databases. The Human Proteome Survey Database (HumanPSD) compiles the fundamental properties of more than 25 000 characterized mammalian proteins. HumanPSD includes clear, concise and current protein descriptions (Title Lines), the protein sequence, calculated physical properties, precomputed BLAST alignments, controlled-vocabulary protein properties and Gene Ontology terms, and a list of published references. Each report also contains expression data, Pfam domain information and an associated Mouse Mutant Phenotype section describing behavioral, physiological and cellular phenotypes for over 1500 mouse mutant phenotypes. GPCR-PD contains more than 3200 Protein Reports from the three mammalian species for G protein-coupled receptors, their protein ligands, associated G-proteins and their downstream signaling proteins. In addition to the features described above, each GPCR-PD Protein Report displays annotations of experimental findings from over 10 000 publications. These databases provide important new volumes of Proteome's BioKnowledge Library (http://www.incyte.com), integrating protein information from model organisms with the human proteome.

Amino Acid Sequence↗
PubMed2026

Mapping the causal chain from genetic risk variants to lipid dysmetabolism in Parkinson's disease.

The molecular pathways linking genetic variants to Parkinson's disease (PD) onset and progression remain incompletely defined; however, risk alleles in multiple genes, including GBA1, strongly implicate lipid metabolism. To systematically identify causal biomarker signatures, we analysed comprehensive metabolome profiles from blood plasma in 149 PD patients and 150 controls, along with complementary genetic, RNA-sequencing and metabolic data from other available clinical and pathologic cohorts. Using colocalization and summary-data-based Mendelian randomization, we tested whether expression and metabolic quantitative trait loci mediate the association between implicated genetic variants and PD risk. We further integrated differential metabolomics and proteomics from blood and brain to reveal pertinent mechanisms. We show that common PD risk variants at the serine palmitoyltransferase small subunit B (SPTSSB) locus, a key regulator of de novo sphingolipid biosynthesis, are associated with increased SPTSSB brain expression and elevated plasma ceramides. Additional analyses strongly support our hypothesis that a common SPTSSB causal variant is responsible for PD risk as well as the expression and metabolic quantitative trait loci. Multiple sphingolipids and fatty acid derivatives were perturbed in PD, and we identified both unique and shared features with the Alzheimer's disease metabolome. A PD acylcarnitine signature was further replicated in human post-mortem brain tissue, when comparing those with or without preclinical Lewy body pathology. Integrated analysis of complementary brain proteomic profiles revealed dysregulation of mitochondrial processes dependent on acylcarnitines, including fatty acid beta-oxidation, the tricarboxylic acid cycle and oxidative phosphorylation. Our results identify promising biomarkers and reveal a causal chain linking genetic variation to altered gene/protein expression, lipid dysmetabolism, and the manifestation of PD.

Humans↗
PubMed2006

Integrative investigation of metabolic and transcriptomic data.

BACKGROUND: New analysis methods are being developed to integrate data from transcriptome, proteome, interactome, metabolome, and other investigative approaches. At the same time, existing methods are being modified to serve the objectives of systems biology and permit the interpretation of the huge datasets currently being generated by high-throughput methods. RESULTS: Transcriptomic and metabolic data from chemostat fermentors were collected with the aim of investigating the relationship between these two data sets. The variation in transcriptome data in response to three physiological or genetic perturbations (medium composition, growth rate, and specific gene deletions) was investigated using linear modelling, and open reading-frames (ORFs) whose expression changed significantly in response to these perturbations were identified. Assuming that the metabolic profile is a function of the transcriptome profile, expression levels of the different ORFs were used to model the metabolic variables via Partial Least Squares (Projection to Latent Structures--PLS) using PLS toolbox in Matlab. CONCLUSION: The experimental design allowed the analyses to discriminate between the effects which the growth medium, dilution rate, and the deletion of specific genes had on the transcriptome and metabolite profiles. Metabolite data were modelled as a function of the transcriptome to determine their congruence. The genes that are involved in central carbon metabolism of yeast cells were found to be the ORFs with the most significant contribution to the model.

Algorithms↗
PubMed2026

Polygenic risk scores associate with asthma phenotypes and proteomic analyses implicate IL1R1 in two family-based studies.

Despite its high prevalence and the discovery of hundreds of genetic associations, the genetic determinants and heterogeneous manifestations of asthma remain incompletely understood. Incorporating polygenic risk scores (PRS) into asthma research offers a powerful approach to quantify inherited susceptibility, refine risk profiles, and advance mechanistic understanding of disease development. For this study, we leveraged whole-genome sequencing (WGS) data from two family-based cohorts of childhood asthma - the Genetics of Asthma in Costa Rica Study (GACRS) and the Childhood Asthma Management Program (CAMP) - to examine the transmission profiles of externally derived asthma PRS and their associations with clinical phenotypes in children with asthma. To further elucidate molecular mechanisms, we integrated large-scale external genome-wide association study (GWAS) summary statistics and genetic prediction models of protein abundance in a two-step proteome-wide association study (PWAS) of asthma. Our findings provide robust evidence supporting the validity of externally derived asthma PRS (asthma PRS association p-value p = 10-24 [GACRS and CAMP trios combined] for the Global Biobank Meta-analysis Initiative [GBMI]) and reveal consistent associations with spirometry measures and atopy markers across both studies, as 13 of 21 traits (62%) were significantly associated with the GBMI-PRS in the meta-analysis after multiple-testing correction. Moreover, the results of the integrative proteomic analysis implicate IL-1 signaling in the etiology of asthma, reinforcing the candidacy of IL1R1 antagonists for drug repurposing.

Journal Article↗
PubMed2002

De novo peptide sequencing and quantitative profiling of complex protein mixtures using mass-coded abundance tagging.

Proteomic studies require efficient, robust, and practical methods of characterizing proteins present in biological samples. Here we describe an integrated strategy for systematic proteome analysis based on differential guanidination of C-terminal lysine residues on tryptic peptides followed by capillary liquid chromatography-electrospray tandem mass spectrometry. The approach, termed mass-coded abundance tagging (MCAT), facilitates the automated, large-scale, and comprehensive de novo determination of peptide sequence and relative quantitation of proteins in biological samples in a single analysis. MCAT offers marked advantages as compared with previously described methods and is simple, economic, and effective when applied to complex proteomic mixtures. MCAT is used to identify proteins, including polymorphic variants, from complex mixtures and measure variation in protein levels from diverse cell types.

Algorithms↗
PubMed2005

The nutritional phenotype in the age of metabolomics.

The concept of the nutritional phenotype is proposed as a defined and integrated set of genetic, proteomic, metabolomic, functional, and behavioral factors that, when measured, form the basis for assessment of human nutritional status. The nutritional phenotype integrates the effects of diet on disease/wellness and is the quantitative indication of the paths by which genes and environment exert their effects on health. Advances in technology and in fundamental biological knowledge make it possible to define and measure the nutritional phenotype accurately in a cross section of individuals with various states of health and disease. This growing base of data and knowledge could serve as a resource for all scientific disciplines involved in human health. Nutritional sciences should be a prime mover in making key decisions that include: what environmental inputs (in addition to diet) are needed; what genes/proteins/metabolites should be measured; what end-point phenotypes should be included; and what informatics tools are available to ask nutritionally relevant questions. Nutrition should be the major discipline establishing how the elements of the nutritional phenotype vary as a function of diet. Nutritional sciences should also be instrumental in linking the elements that are responsive to diet with the functional outcomes in organisms that derive from them. As the first step in this initiative, a prioritized list of genomic, proteomic, and metabolomic as well as functional and behavioral measures that defines a practically useful subset of the nutritional phenotype for use in clinical and epidemiological investigations must be developed. From this list, analytic platforms must then be identified that are capable of delivering highly quantitative data on these endpoints. This conceptualization of a nutritional phenotype provides a concrete form and substance to the recognized future of nutritional sciences as a field addressing diet, integrated metabolism, and health.

Diet↗
PubMed2006

Integration of a two-phase partition method into proteomics research on rat liver plasma membrane proteins.

To comprehensively identify proteins of the rat liver plasma membrane (PM), we have adopted a proteomics strategy that utilizes sucrose density centrifugation in conjunction with aqueous two-phase partition for plasma membrane isolation, followed by SDS-PAGE, mass spectrometry and bioinformatics. Western blot analysis showed that this method results in highly purified plasma membrane fractions, which is a key to successful plasma membrane proteomics. The PM proteins were separated by SDS-PAGE and digested with trypsin. Through nano-ESI-LC MS/MS analysis we identified 428 rat liver membrane proteins, of which 304 had a gene ontology (GO) annotation indicating a cellular component, and 204 (67%) of the latter were known integral membrane proteins or membrane-associated proteins. In addition to proteins known to be associated with the plasma membrane, several hypothetical proteins have also been identified. This study not only provides a tool to study plasma membrane proteins with low levels of contamination, but also provides a data set for proteins of high to moderate abundance in rat liver plasma membranes, thus allowing for more comprehensive characterization of membrane proteins and a better understanding of membrane dynamics.

Animals↗
PubMed2003

Toxicogenomics of bromobenzene hepatotoxicity: a combined transcriptomics and proteomics approach.

Toxicogenomics is a novel approach integrating the expression analysis of thousands of genes (transcriptomics) or proteins (proteomics) with classical methods in toxicology. Effects at the molecular level are related to pathophysiological changes of the organisms, enabling detailed comparison of mechanisms and early detection and prediction of toxicity. This report addresses the value of the combined use of transcriptomics and proteomics technologies in toxicology. Acute hepatotoxicity was induced in rats by bromobenzene administration resulting in depleted glutathione levels and reduced average body weights, 24hr after dosage. These physiological symptoms coincided with many changes of hepatic mRNA and protein content. Gene induction confirmed involvement of glutathione-S-transferase isozymes and epoxide hydrolase in bromobenzene metabolism and identified many genes possibly relevant in bromobenzene toxicity. Observed glutathione depletion coincided with induction of the key enzyme in glutathione biosynthesis, gamma-glutamylcysteine synthetase. Oxidative stress was apparent from strong upregulation of heme oxygenase, peroxiredoxin 1 and other genes. Bromobenzene-induced protein degradation was suggested from two-dimensional gel electrophoresis, upregulated mRNA levels for proteasome subunits and lysosomal cathepsin L, whereas also genes were upregulated with a role in protein synthesis. Both protein and gene expression profiles from treated rats were clearly distinct from controls as shown by principal component analysis, and several proteins found to significantly change upon bromobenzene treatment were identified by mass spectrometry. A modest overlap in results from proteomics and transcriptomics was found. This work indicates that transcriptomics and proteomics technologies are complementary to each other and provide new possibilities in molecular toxicology.

Animals↗
PubMed2025

Hepatocyte proteome destabilization and novel targets for PFASs unveiled through combined thermal proteome profiling and deep transfer learning.

Identifying protein targets for per- and polyfluoroalkyl substances (PFASs) is essential to understand their toxicity and health risks. However, knowledge about their interacting proteins is limited since reliable identification methods are lacking. We developed an integrated approach combining thermal proteome profiling (TPP) and deep transfer learning (DTL) modeling to efficiently identify cellular targets of PFAS. TPP measured PFAS binding proteins and the affinities by nanospray liquid chromatography tandem mass spectrometry, while DTL models were constructed to predict PFAS-protein affinities using neural network algorithms. TPP results revealed that PFASs uniquely destabilized the proteome of HepG2 cells, unlike the stabilizing effects by other xenobiotics. Key protein targets for three representative PFASs (PFOA, GenX and Novec 649) were identified, which exhibited weak binding affinities (median EC50 &#x2248; 30&#x202f;&#x3bc;M). The number of protein targets increased with molecular weights among the three PFASs. The DTL model achieved a higher Pearson correlation coefficient of 0.89, and reduced mean squared errors by 54&#x202f;% over previous models for drug-protein interactions. Notably, TPP and DTL jointly pinpointed ribosomal proteins as novel targets of GenX, potentially linking it to cell apoptosis through disrupted protein synthesis. Biolayer interferometry validated GenX binding to RPL4 protein, driven by electrostatic interactions and halogen bonds. This integrated approach effectively uncovers novel PFASs targets, advancing insights into their adverse health effects.

Humans↗
PubMed2004

System-based proteomic analysis of the interferon response in human liver cells.

BACKGROUND: Interferons (IFNs) play a critical role in the host antiviral defense and are an essential component of current therapies against hepatitis C virus (HCV), a major cause of liver disease worldwide. To examine liver-specific responses to IFN and begin to elucidate the mechanisms of IFN inhibition of virus replication, we performed a global quantitative proteomic analysis in a human hepatoma cell line (Huh7) in the presence and absence of IFN treatment using the isotope-coded affinity tag (ICAT) method and tandem mass spectrometry (MS/MS). RESULTS: In three subcellular fractions from the Huh7 cells treated with IFN (400 IU/ml, 16 h) or mock-treated, we identified more than 1,364 proteins at a threshold that corresponds to less than 5% false-positive error rate. Among these, 54 were induced by IFN and 24 were repressed by more than two-fold, respectively. These IFN-regulated proteins represented multiple cellular functions including antiviral defense, immune response, cell metabolism, signal transduction, cell growth and cellular organization. To analyze this proteomics dataset, we utilized several systems-biology data-mining tools, including Gene Ontology via the GoMiner program and the Cytoscape bioinformatics platform. CONCLUSIONS: Integration of the quantitative proteomics with global protein interaction data using the Cytoscape platform led to the identification of several novel and liver-specific key regulatory components of the IFN response, which may be important in regulating the interplay between HCV, interferon and the host response to virus infection.

Cell Line, Tumor↗
PubMed2025

Admixture-mapping analysis reveals genetic determinants of the human plasma proteome.

Protein profiling and genetic findings can be integrated to define the genetic architecture of the circulating proteome in chronic diseases. Most self-identified African American (AA) individuals have both African and European genetic ancestry. Admixture mapping can detect genomic association regions in which causal variants exist with substantial differences in allele frequency or effect sizes between genetic ancestries. We performed admixture mapping of the circulating proteome in 1,989 participants from the Jackson Heart Study (JHS), investigating the relation of local African ancestry within genomic regions with levels of circulating proteins. We conditioned protein-local ancestry association models on variants previously found to be associated with those proteins in genome-wide association studies (GWASs). We replicated findings in 196 AA participants from the Multi-Ethnic Study of Atherosclerosis (MESA). 62 proteins were associated with local African ancestry. 21 of 62 remained statistically significant after conditioning on protein-associated variants observed in previous GWASs. 48 of 54 available protein-local ancestry associations were replicated in the MESA. Proteins associated with local African ancestry included chemokines, factors associated with vascular biology and inflammation, and other biologically interesting proteins. Admixture associations unexplained by previously reported protein-associated variants in conditional analysis suggest the existence of causal variants missed by standard GWAS techniques.

Aged↗
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↗