SEARCH · PubMed Health

Results for “Integrated proteomics”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 289 records · Page 16Linked to original sources

PubMed2005

Functional Genomics meets neurodegenerative disorders. Part II: application and data integration.

The transcriptomic and proteomic techniques presented in part I (Functional Genomics meets neurodegenerative disorders. Part I: transcriptomic and proteomic technology) of this back-to-back review have been applied to a range of neurodegenerative disorders, including Huntington's disease (HD), Prion diseases (PrD), Creutzfeldt-Jakob disease, amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD), frontotemporal dementia (FTD) and Parkinson's disease (PD). Samples have been derived either from human brain and cerebrospinal fluid, tissue culture cells or brains and spinal cord of experimental animal models. With the availability of huge data sets it will firstly be a major challenge to extract meaningful information and secondly, not to obtain contradicting results when data are collected in parallel from the same source of biological specimen using different techniques. Reliability of the data highly depends on proper normalization and validation both of which are discussed together with an outlook on developments that can be anticipated in the future and are expected to fuel the field. The new insight undoubtedly will lead to a redefinition and subdivision of disease entities based on biochemical criteria rather than the clinical presentation. This will have important implications for treatment strategies.

Animals↗

PubMedDate not supplied

Integration of Jacobson's pellicle method into proteomic strategies for plasma membrane proteins.

A modified form of the cationic colloidal silica technique for plasma membrane isolation has been combined with SDS-PAGE, mass spectrometry, and bioinformatics for evaluation as a proteomics strategy with human multiple myeloma cells and human breast cancer cells. On the basis of Western blots, half of the protein isolated is estimated to come from the plasma membrane. Forty-three percent of the 366 proteins identified by mass spectrometry had been previously classified as plasma membrane proteins. Thirty proteins previously categorized as hypothetical membrane proteins are now reported to be expressed.

Breast Neoplasms↗

PubMed2006

The proteome: structure, function and evolution.

This paper reports two studies to model the inter-relationships between protein sequence, structure and function. First, an automated pipeline to provide a structural annotation of proteomes in the major genomes is described. The results are stored in a database at Imperial College, London (3D-GENOMICS) that can be accessed at www.sbg.bio.ic.ac.uk. Analysis of the assignments to structural superfamilies provides evolutionary insights. 3D-GENOMICS is being integrated with related proteome annotation data at University College London and the European Bioinformatics Institute in a project known as e-protein (http://www.e-protein.org/). The second topic is motivated by the developments in structural genomics projects in which the structure of a protein is determined prior to knowledge of its function. We have developed a new approach PHUNCTIONER that uses the gene ontology (GO) classification to supervise the extraction of the sequence signal responsible for protein function from a structure-based sequence alignment. Using GO we can obtain profiles for a range of specificities described in the ontology. In the region of low sequence similarity (around 15%), our method is more accurate than assignment from the closest structural homologue. The method is also able to identify the specific residues associated with the function of the protein family.

Computational Biology↗

PubMed2026

Plasma proteomics: considerations for preanalytical variability; a systematic review with narrative synthesis.

BACKGROUND: The plasma proteome (PP) is a dynamic system subject to pathology-associated changes and a focus for novel disease biomarker discovery. Disease-related PP research assumes protein concentrations in test specimens accurately reflect the in vivo milieu. However, measures to maintain the physicochemical integrity of the proteome before assay are often rudimentary, poorly described, or lacking standardisation in published studies. Contrastingly, in laboratory medicine, there is an expectation that errors in the so-called "preanalytical phase" (PAP) that impact patient results are understood, monitored, and mitigated against, while also being well described in research publications. There is therefore scope for good practice from laboratory medicine to inform PP research workflows. This review considers factors in the PAP which may impact the validity of PP results. CONTENT: A systematic review was conducted per PRISMA guidelines, limited to English-language peer-reviewed studies (2014-2024). Candidate studies were imported, screened, and managed using Covidence systematic review software. SUMMARY: 15 eligible studies were reviewed, covering many relevant processes. 11 studies reported statistically significant differences in PP due to factors in the PAP. Temperature and time-to-processing were the most commonly reported factors affecting the PP, with significant effects reported in 8 studies. OUTLOOK: PAP variability can significantly affect results in PP studies. Careful consideration of the effect of each stage of the PAP is needed when working with the PP. In multicenter studies, pre-defined and research question-specific sample processing workflows are essential for reducing PAP variability, which helps ensure the validity of PP studies.

Humans↗

PubMed2026

Divergent evolutionary strategies in spider venoms: A comparative proteomic profiling of four sympatric species from Yunnan.

Spider venoms comprise complex cocktails of bioactive molecules evolved for predation and defense, representing a valuable resource for biological research and pharmaceutical discovery. In this study, we performed a systematic analysis of venom gland extracts from four common spider species indigenous to Yunnan, China: Agelena limbata, Hippasa lycosina, Lycosa grahami, and Sinopoda pengi. Using an integrated transcriptomic and proteomic targeted profiling approach, we successfully annotated 141 distinct toxins. Comparative analysis revealed significant interspecific heterogeneity, suggesting distinct evolutionary trajectories and "weapon system economics." Both A. limbata and L. grahami exhibited a "peptide-dominant" profile anchored by neurotoxic peptides and isomerases, optimized for rapid chemical paralysis. In contrast, S. pengi displayed a distinct "protein-dominant" signature enriched with high-molecular-weight enzymes and CAP superfamily proteins, likely functioning to facilitate tissue degradation and toxin diffusion. Occupying an intermediate position, H. lycosina demonstrated a hybrid composition. These findings suggest that although these species share the same geographical range, their venom systems have undergone divergent evolutionary adaptations driven by specific ecological niches and hunting strategies. This study represents the first systematic proteomic characterization of these venom components, providing a valuable reservoir of molecular candidates while highlighting the bioinformatic nuances of analyzing whole-gland homogenates.

Animals↗

PubMed2005

Application of immobilized metal affinity chromatography in proteomics.

It has been proved that the progress of proteomics is mostly determined by the development of advanced and sensitive protein separation technologies. Immobilized metal affinity chromatography (IMAC) is a powerful protein fractionation method used to enrich metal-associated proteins and peptides. In proteomics, IMAC has been widely employed as a prefractionation method to increase the resolution in protein separation. The combination of IMAC with other protein analytical technologies has been successfully utilized to characterize metalloproteome and post-translational modifications. In the near future, newly developed IMAC integrated with other proteomic methods will greatly contribute to the revolution of expression, cell-mapping and structural proteomics.

Animals↗

PubMed2026

Multidimensional Protein Corona Analysis Toward Predictive Nano-Bio Interface Design.

Nanoparticles entering biological fluids are rapidly coated by proteins and other biomolecules, converting their synthetic surfaces into biologically active nano-bio interfaces. These coronas regulate colloidal stability, immune recognition, cellular uptake, biodistribution, pharmacokinetics, cargo delivery, and toxicity. Yet a protein list obtained by mass spectrometry captures only part of this interface. Corona identity and function are also shaped by protein organization, binding stability, exchange dynamics, conformational changes, and molecular accessibility. Here, we discuss recent progress in protein corona isolation and analysis from a question-oriented analytical perspective, with emphasis on how centrifugation, magnetic recovery, affinity- or chemistry-enabled capture, chromatography, filtration, and field-flow fractionation (FFF) influence the fidelity, integrity, and comparability of recovered coronas. We then examine how proteomic profiling can be integrated with binding measurements, interfacial structural analysis and functional validation to distinguish descriptive corona signatures from biologically meaningful mechanisms. We further consider how biofluid composition, disease state, tissue interfaces and cellular environments remodel corona identity, presentation, and bioactivity. Finally, we argue that standardized reporting, computational modeling, and AI-enabled approaches are essential for converting protein corona datasets into reproducible and predictive knowledge that can guide the design of drug delivery systems and precision nanomedicines.

Protein Corona↗

PubMed2006

Scaling and normalization effects in NMR spectroscopic metabonomic data sets.

Considerable confusion appears to exist in the metabonomics literature as to the real need for, and the role of, preprocessing the acquired spectroscopic data. A number of studies have presented various data manipulation approaches, some suggesting an optimum method. In metabonomics, data are usually presented as a table where each row relates to a given sample or analytical experiment and each column corresponds to a single measurement in that experiment, typically individual spectral peak intensities or metabolite concentrations. Here we suggest definitions for and discuss the operations usually termed normalization (a table row operation) and scaling (a table column operation) and demonstrate their need in 1H NMR spectroscopic data sets derived from urine. The problems associated with "binned" data (i.e., values integrated over discrete spectral regions) are also discussed, and the particular biological context problems of analytical data on urine are highlighted. It is shown that care must be exercised in calculation of correlation coefficients for data sets where normalization to a constant sum is used. Analogous considerations will be needed for other biofluids, other analytical approaches (e.g., HPLC-MS), and indeed for other "omics" techniques (i.e., transcriptomics or proteomics) and for integrated studies with "fused" data sets. It is concluded that data preprocessing is context dependent and there can be no single method for general use.

Algorithms↗

PubMed2001

Application of proteomics to the study of cardiovascular biology.

Proteomics involves the integration of a number of technologies with the aim of analyzing the complete complement of proteins expressed by a biological system in response to various stimuli and/or under different physiological or pathophysiological conditions. Recent technical improvements to the methods employed for protein separation and protein identification have resulted in a dramatic increase in the number of proteomics-based research projects. More importantly, it has become readily apparent that examining changes in the proteome offers insight into understanding cellular and molecular mechanisms that cannot be obtained through genomic analysis. There are numerous examples of cardiovascular functions whose molecular pathways are mediated through post-translational processes such as phosphorylation. The use of proteomics offers the ability to simultaneously monitor the changes in protein expression and/or cell signaling pathways in response to such conditions as cardiac hypertrophy and heart failure. Together with complementary genomic data, proteomics-based research can greatly increase our understanding of cardiovascular biology.

Cardiovascular Diseases↗

PubMed2006

Microfluidic liquid chromatography system for proteomic applications and biomarker screening.

A microfluidic liquid chromatography (LC) system for proteomic investigations that integrates all the necessary components for stand-alone operation, i.e., pump, valve, separation column, and electrospray interface, is described in this paper. The overall size of the LC device is small enough to enable the integration of two fully functional separation systems on a 3 in. x 1 in. glass microchip. A multichannel architecture that uses electroosmotic pumping principles provides the necessary functionality for eluent propulsion and sample valving. The flow rates generated within these chips are fully consistent with the requirements of nano-LC platforms that are routinely used in proteomic applications. The microfluidic device was evaluated for the analysis of a protein digest obtained from the MCF7 breast cancer cell line. The cytosolic protein extract was processed according to a shotgun protocol, and after tryptic digestion and prefractionation using strong cation exchange chromatography (SCX), selected sample subfractions were analyzed with conventional and microfluidic LC platforms. Using similar experimental conditions, the performance of the microchip LC was comparable to that obtained with benchtop instrumentation, providing an overlap of 75% in proteins that were identified by more than two unique peptides. The microfluidic LC analysis of a protein-rich SCX fraction enabled the confident identification of 77 proteins by using conventional data filtering parameters, of 39 proteins with p < 0.001, and of 5 proteins that are known to be cancer-specific biomarkers, demonstrating thus the potential applicability of these chips for future high-throughput biomarker screening applications.

Biomarkers, Tumor↗

PubMed2006

Cytomics - importance of multimodal analysis of cell function and proliferation in oncology.

Cancer is a highly complex and heterogeneous disease involving a succession of genetic changes (frequently caused or accompanied by exogenous trauma), and resulting in a molecular phenotype that in turn results in a malignant specification. The development of malignancy has been described as a multistep process involving self-sufficiency in growth signals, insensitivity to antigrowth signals, evasion of apoptosis, limitless replicative potential, sustained angiogenesis, and finally tissue invasion and metastasis. The quantitative analysis of networking molecules within the cells might be applied to understand native-state tissue signalling biology, complex drug actions and dysfunctional signalling in transformed cells, that is, in cancer cells. High-content and high-throughput single-cell analysis can lead to systems biology and cytomics. The application of cytomics in cancer research and diagnostics is very broad, ranging from the better understanding of the tumour cell biology to the identification of residual tumour cells after treatment, to drug discovery. The ultimate goal is to pinpoint in detail these processes on the molecular, cellular and tissue level. A comprehensive knowledge of these will require tissue analysis, which is multiplex and functional; thus, vast amounts of data are being collected from current genomic and proteomic platforms for integration and interpretation as well as for new varieties of updated cytomics technology. This overview will briefly highlight the most important aspects of this continuously developing field.

Cell Division↗

PubMed2006

Construction of a nasopharyngeal carcinoma 2D/MS repository with Open Source XML database--Xindice.

BACKGROUND: Many proteomics initiatives require integration of all information with uniformcriteria from collection of samples and data display to publication of experimental results. The integration and exchanging of these data of different formats and structure imposes a great challenge to us. The XML technology presents a promise in handling this task due to its simplicity and flexibility. Nasopharyngeal carcinoma (NPC) is one of the most common cancers in southern China and Southeast Asia, which has marked geographic and racial differences in incidence. Although there are some cancer proteome databases now, there is still no NPC proteome database. RESULTS: The raw NPC proteome experiment data were captured into one XML document with Human Proteome Markup Language (HUP-ML) editor and imported into native XML database Xindice. The 2D/MS repository of NPC proteome was constructed with Apache, PHP and Xindice to provide access to the database via Internet. On our website, two methods, keyword query and click query, were provided at the same time to access the entries of the NPC proteome database. CONCLUSION: Our 2D/MS repository can be used to share the raw NPC proteomics data that are generated from gel-based proteomics experiments. The database, as well as the PHP source codes for constructing users' own proteome repository, can be accessed at http://www.xyproteomics.org/.

Carcinoma↗

PubMed2026

Mitochondrial homeodynamics in ageing: mechanisms, resilience, and interventions.

Mitochondria integrate bioenergetics, redox signalling, calcium handling, biosynthesis, apoptosis, and stress responses. Their contribution to ageing depends less on any single pathway than on the ability to sustain these functions through continuous maintenance, remodelling, and inter-organelle communication. This review proposes mitochondrial homeodynamics as a systems-level framework for that ability, which rests not on static preservation but on three linked capacities. Maintenance safeguards mitochondrial genome, proteome, and membrane integrity. Adaptation adjusts metabolism and remodels network and cristae architecture to match changing demand. Recovery restores function and reserve after challenge. These capacities emerge from mitochondrial quality control, network and cristae remodelling, biogenesis, mitophagy, retrograde stress signalling, and inter-organelle communication. So defined, mitochondrial dysfunction becomes a measurable loss of capacity rather than a descriptive category. Ageing erodes these capacities in tissue- and context-specific ways, which reduces physiological reserve, slows recovery after stress, and amplifies sterile inflammation. The mechanisms underlying these capacities, the biomarkers that report them, and the interventions proposed to preserve them are evaluated in turn. Exercise provides the strongest human evidence for coordinated mitochondrial and functional adaptation, whereas evidence for energy restriction, NAD+ precursors, mitophagy-supporting compounds, and mitochondria-targeted agents remains heterogeneous and endpoint-specific. No mitochondrial intervention has been shown to slow ageing or extend lifespan in healthy humans, and movement of a biomarker towards a younger reference value does not establish rejuvenation. Progress will require dynamic measures of maintenance, adaptation, and recovery, obtained in defined tissues and interpreted alongside clinically meaningful outcomes.

Humans↗

PubMed2026

Multiomics approaches to cardiovascular disease: technological innovations and clinical translation.

Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, reflecting a persistent gap between clinical phenotyping and the molecular mechanisms that govern disease initiation, progression, and interindividual variability. Recent advances in emerging technologies have fundamentally reshaped cardiovascular physiology by enabling high-resolution, cross-layer profiling of the heart and vasculature across genomic, epigenomic, transcriptomic, proteomic, metabolomic, lipidomic, glycomic, and fluxomic layers, increasingly at single-cell and spatial resolution. These approaches reveal CVD as a coordinated, multilayered process driven by dynamic interactions among cell types, regulatory programs, and metabolic states, rather than isolated gene-level defects. In this review, we synthesize how emerging multiomic, computational, and functional genomic technologies are redefining the study of cardiovascular disease across molecular, cellular, and tissue levels. We highlight recent innovations in single-cell and spatial atlases, long-read sequencing, proteomics and metabolomics, integrative data modeling, and functional omics approaches, including genome-scale perturbation screens and single-cell perturbation frameworks. These platforms enable mechanistic dissection of regulatory circuits, distinguish primary disease drivers from secondary adaptations, and directly assess therapeutic reversibility, advancing the field beyond associative biomarker discovery toward mechanism-guided target prioritization. We further discuss key methodological and translational challenges accompanying high-dimensional cardiovascular data, including preanalytical variability, control selection, temporal misalignment across molecular layers, population diversity, and reference bias. By integrating technological innovation with computational rigor and functional validation, this review frames emerging omics-enabled strategies as a unified, physiologically grounded framework for translating molecular insight into clinically meaningful cardiovascular phenotypes and advancing precision cardiovascular medicine.

Humans↗

PubMed2026

NR3C1 Modulates Wnt Signalling to Influence the Invasiveness and Immune Features of Nonfunctioning Invasive Pituitary Adenomas.

Pituitary adenomas (PAs) are common intracranial tumours, and invasiveness in nonfunctioning invasive pituitary adenomas (NIPAs) predicts poor prognosis. The molecular mechanisms driving this phenotype remain unclear. This study explored the role of nuclear receptor subfamily 3 group C member 1 (NR3C1) in NIPA invasiveness and its regulation of Wnt signalling. mRNA expression profiles of 32 PA samples were generated by RNA-seq, and proteomic data from 19 samples were obtained by mass spectrometry. Immune-related differentially expressed genes (DEGs) were retrieved from GeneCards. Weighted gene coexpression network analysis identified modules and hub genes linked to invasiveness, while machine learning methods (support vector machine, LASSO, random forest) prioritised key genes. Gene set enrichment analysis (GSEA) assessed pathways associated with candidate gene expression. NR3C1 expression and function were validated by immunohistochemistry, Western blotting and invasion assays. Integration of transcriptomic, proteomic and immune-related datasets yielded 11 overlapping genes, with NR3C1 emerging as the top candidate. NR3C1 was significantly upregulated in NIPAs and demonstrated good discriminatory power by ROC analysis. GSEA associated high NR3C1 expression with Wnt pathway activation. Functional experiments confirmed that NR3C1 overexpression enhances the invasive capacity of PA cells. NR3C1 promotes the invasive phenotype of NIPAs by activating Wnt signalling. These findings suggest NR3C1 as a potential biomarker and therapeutic target for invasive pituitary adenomas.

Humans↗

PubMed2026

Plasma Multiomics Links Early-Life Adversity to Disease and Cardiometabolic Health: A Population-Based Cohort Study.

BACKGROUND: Childhood adversity (CA) is associated with increased cardiovascular and cardiometabolic risk, but the molecular mechanisms remain unclear. We aimed to identify CA-related metabolomic and proteomic signatures and evaluate their roles in linking CA to incident diseases. METHODS: This prospective cohort study included 153 225 participants aged 48 to 64 years. CA was assessed using the Childhood Trauma Screener-5, capturing cumulative (0-5 domains) and individual adversity exposures. Plasma metabolomics and proteomics data were integrated to derive CA-related molecular signatures. Cox proportional hazards model was used to evaluate associations between CA, molecular signatures, and 58 incident diseases and mortality. Mediation analyses quantified the role of multiomics signatures. RESULTS: Each additional CA domain was associated with higher risk of 49 of 58 incident diseases (hazard ratios [HRs], 1.024-1.338) and a 7.3% higher all-cause mortality risk. Focusing on cardiometabolic health, the CA-related metabolic and proteomic signatures were independently associated with incident disease. Per 1-SD increase in the cumulative CA metabolic signature, the highest observed HR was 1.313 (95% CI, 1.278-1.349) for incident diabetes, and the risk for hypertension was also increased (HR, 1.136 95% CI, 1.114-1.159). Similarly, the proteomic signature was strongly associated with incident diabetes (HR, 1.645 95% CI, 1.489-1.818) and hypertension (HR, 1.223 95% CI, 1.155-1.295). The cumulative CA metabolic and proteomic signatures mediated up to 25.5% and 46.8% of the association with hypertension, respectively. CONCLUSIONS: CA is associated with a broad spectrum of diseases and mortality, with particularly strong links to cardiometabolic health. Multiomics signatures partially mediated these associations.

Humans↗

PubMed2004

Genomic and proteomic profiling for biomarkers and signature profiles of toxicity.

Toxicity profiling measures and compares all gene expression changes among biological samples after toxicant exposure. Toxicity profiling with DNA microarrays to measure all mRNA transcripts (transcriptomics), or by global separation and identification of proteins (proteomics), has led to the discovery of better descriptors of toxicity, toxicant classification and exposure monitoring than current indicators. A shared goal in transcript and proteomic profiling is the development of biomarkers and signatures of chemical toxicity. In this review, biomarkers and signature profiles are described for specific chemical toxicants that affect target organs such as liver, kidney, neural tissues, gastrointestinal tract and skeletal muscle, for specific disease models such as cancer and inflammation, and for unique chemical-protein adducts underlying cell injury. The recent introduction of toxicogenomics databases support researchers in sharing, analyzing, visualizing and mining expression data, assist the integration of transcriptomics, proteomics and toxicology datasets, and eventually will permit in silico biomarker and signature pattern discovery.

Animals↗

PubMed2026

Genomic and proteogenomic insights into Spontaneous Coronary Artery Dissection (SCAD): A systematic review of emerging multi-omic evidence.

BACKGROUND: Spontaneous coronary artery dissection (SCAD) is a major cause of myocardial infarction in young women without traditional cardiovascular risk factors (Hayes et al., 2018; Adlam et al., 2018 [1, 2]). Despite growing awareness, its biological underpinnings remain incompletely understood, and clinical management is largely based on observational evidence rather than mechanistic insight (Saw et al., 2014; Lettieri et al., 2015; Steg et al., 2024 [3-5]). OBJECTIVES: To systematically integrate genomic, epitranscriptomic, proteomic, and metabolomic data in order to characterize the multi-omic architecture of SCAD and identify potential biomarkers and therapeutic targets. METHODS: A systematic review was conducted in accordance with the PRISMA 2020 statement (Arbelo et al., 2023 [6]). PubMed/MEDLINE was searched for original studies investigating genomic and multi-omic features of SCAD. Data were extracted on study design, patient characteristics, identified variants, circulating biomarkers, and implicated biological pathways. Functional enrichment analysis was performed using the DAVID bioinformatics resource (Page et al., 2021 [7]). RESULTS: A total of 16 studies were included. Genome-wide association studies consistently identified susceptibility loci related to arterial structure and extracellular matrix integrity, including ADAMTSL4, PHACTR1/EDN1, LRP1, and FBN1 (Huang et al., 2009; Saw et al., 2020; Turley et al., 2020 [8-10]). Rare variant analyses further supported the role of genes involved in extracellular matrix remodeling and vascular smooth muscle cell function, including COL3A1, COL4A1/2, SMAD3, and TLN1 (Adlam et al., 2023; Turley et al., 2021, 2019; Carss et al., 2020; Zekavat et al., 2022; Wang et al., 2022 [11-16]), while ancestry-specific signals such as TSR1 variants were observed in distinct populations (Turley et al., 2023 [17]). Proteogenomic approaches linked genetic susceptibility loci to circulating proteins involved in matrix remodeling and inflammation, including cathepsin B and ECM1 (Maioli et al., 2010 [18]). Epitranscriptomic analyses identified differential microRNA expression profiles associated with vascular injury and repair pathways (Sun et al., 2019 [19]). CONCLUSIONS: SCAD is characterized by a complex, multi-layered biological architecture involving genetic susceptibility, extracellular matrix dysregulation, and vascular signaling pathways. Integration of multi-omic data provides novel insights into disease mechanisms and highlights potential biomarkers and targets for precision medicine approaches in SCAD.

Animals↗