PubMed Health⌕ Search

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 109 records · Page 6Linked to original sources

Hypothesis: Huntingtin may function in membrane association and vesicular trafficking.

Huntington's disease is a progressive neurodegenerative genetic disorder that is caused by a CAG triplet-repeat expansion in the first exon of the IT15 gene. This CAG expansion results in polyglutamine expansion in the 350 kDa huntingtin protein. The exact function of huntingtin is unknown. Understanding the pathological triggers of mutant huntingtin, and distinguishing the cause of disease from downstream effects, is critical to designing therapeutic strategies and defining long- and short-term goals of therapy. Many studies that have sought to determine the functions of huntingtin by determining huntingtin's protein-protein interactions have been published. Through these studies, huntingtin has been seen to interact with a large number of proteins, and is likely a scaffolding protein for protein-protein interactions. Recently, using imaging, integrative proteomics, and cell biology, huntingtin has been defined as a membrane-associated protein, with activities related to axonal trafficking of vesicles and mitochondria. These functions have also been attributed to some huntingtin-interacting proteins. Additionally, discoveries of a membrane association domain and a palmitoylation site in huntingtin reinforce the fact that huntingtin is membrane associated. In Huntington's disease mouse and fly models, axonal vesicle trafficking is inhibited, and lack of proper uptake of neurotrophic factors may be an important pathological trigger leading to striatal cell death in Huntington's disease. Here we discuss recent advances from many independent groups and methodologies that are starting to resolve the elusive function of huntingtin in vesicle transport, and evidence that suggests that huntingtin may be directly involved in membrane interactions.

Cell Membrane↗

COVID-19 multi-omics reveal organ-specific responses and biomarkers.

OBJECTIVE: Post-COVID-19 syndrome is characterised by persistent immune dysfunction and multi-organ sequelae. This study aimed to characterise the systemic blood molecular landscape induced by SARS-CoV-2 infection and identify prognostic markers linked to skeletal muscle mass loss, a key driver of poor outcomes. METHODS: We enrolled 30 healthy controls and 307 COVID-19 patients, collecting 422 plasma samples for integrated proteomic and metabolomic profiling to investigate organ-specific molecular alterations in COVID-19. RESULTS: We comprehensively mapped the molecular landscape of COVID-19, encompassing immune, tissue-specific, and metabolic perturbations, and delineated their interactions. Focusing on organ-damage-related molecular patterns associated with disease progression and mortality, we found that skeletal muscle mass loss contributed to poor clinical outcomes of COVID-19 (p&#x2009;<&#x2009;0.0001). Dysregulated arginine metabolism emerged as a key metabolic signature in fatal COVID-19 cases, with GLUL, GOT1, and citrulline showing significant correlation with skeletal muscle mass loss. Longitudinal analyses further revealed that reduced citrulline levels underlie the poor outcome of COVID-19 patients with muscle mass loss. These findings were robustly supported through multiple approaches: Mendelian randomization confirmed causal relationships between citrulline depletion, sarcopenia/fat-free mass loss, and COVID-19 mortality (p&#x2009;<&#x2009;0.05), transcriptomic analyses of SARS-CoV-2-infected golden hamsters (GSE231910) provided additional support in enrichment of arginine biosynthesis (FDR&#x2009;<&#x2009;0.05), and in vitro experiments further demonstrated that citrulline depletion promotes pro-inflammatory M1 macrophage polarisation &#x2014; a key immunological feature of critical COVID-19. Leveraging these insights, we developed a skeletal muscle loss-specific prognostic prediction model for COVID-19 using GLUL, GOT1, and citrulline. This model effectively stratified patients into high- and low-risk groups (p&#x2009;=&#x2009;0.035). CONCLUSION: Our study advances the understanding of COVID-19-induced organ pathophysiology and provides a foundation for developing targeted therapeutic strategies for post-COVID sequelae.

COVID-19↗

A spatially resolved genomic-molecular atlas of human white-matter microstructure.

Human white matter has been linked to inherited variation, circulating molecular state and brain disease, but these layers have rarely been mapped onto the same tract anatomy. Here we measured genetic effects along 6,090 atlas-aligned fiber pathways sampled at 609,000 locations in 72,185 UK Biobank participants, and integrated proteomic and metabolomic profiles within the same anatomical frame. Genetic effects were not whole-tract properties: each locus formed a spatial footprint along fiber trajectories, ranging from single locations to broad multi-tract patterns and reflecting regional polygenicity rather than tract heritability. This map identified 258, 186 and 298 previously unreported loci for fractional anisotropy, mean diffusivity and axial diffusivity; spatial patterns replicated in adults and 157 of 315 FA loci replicated in adolescence in ABCD. Mendelian randomization linked localized genetic effects to neurodegenerative and psychiatric traits, with Alzheimer's disease showing directional effects across 12 of 17 tracts. Multi-omic analyses identified 97 proteomic and 161 metabolomic associations, with the broadest signals from lipid metabolites including linoleic acid and phosphatidylcholines. The strongest lipid-metabolite and genetic signals converged in the corpus callosum, placing inherited variation, disease risk and systemic lipid metabolism on the same localized tract segments.

Journal Article↗

Social disconnection integrates genetic and proteomic risks in suicidal ideation and depression.

Suicidal ideation (SI) and major depressive disorder (MDD) are complex psychiatric conditions arising from the interplay of genetic liability, molecular processes, and psychosocial factors. While these dimensions have been extensively studied in isolation, their joint contribution to SI and MDD remains unclear. This study integrates multi-modal data to elucidate these synergistic effects and develop robust models for individual-level risk stratification. Leveraging longitudinal multi-modal data from 13,085 UK Biobank participants, we integrated genomic, proteomic, and social connection profiles. We developed interpretable risk scores using a rigorous supervised machine learning framework encompassing diverse linear and ensemble classifiers. Permutation importance was employed to quantify feature contributions and derive transparent, weighted risk metrics across diverse classifiers. These scores were validated through association, interaction, and mediation analyses. Social connection-based risk scores significantly differentiated cases and controls across the two suicidal ideation phenotypes at 2017 and 2023 with cross-sectional analyses (AUCs: 0.70 - 0.73), outperforming proteomic-only models. Functional dimensions of social connection emerged as the most informative predictors. Longitudinal analyses revealed that social risk scores at baseline predicted suicidal ideation onset six years later, independent of demographic covariates. Interaction analyses demonstrated that polygenic risk for suicide attempt significantly interacted with both social and proteomic risk features in relation to depression. Structural equation models further confirmed that social disconnection acts as a key mediator linking genetic predisposition to MDD and SI. Social disconnection is a critical risk factor mediating the impact of genetic vulnerability on psychiatric outcomes. Integrating social, genetic, and molecular data supports a multilevel framework for risk stratification and highlights the potential of socially oriented interventions to mitigate biological risk.

Humans↗

Integrative metabolomic and proteomic analysis of diabetic kidney disease progression with younger-onset type 2 diabetes.

AIM: Younger-onset type 2 diabetes (YT2D) confers a disproportionately high risk of diabetic kidney disease (DKD), yet early biomarkers and underlying mechanisms remain poorly defined. We aimed to identify metabolites associated with DKD progression and integrate metabolomic and proteomic data to elucidate pathways involved in a multi-ethnic Asian cohort. MATERIALS AND METHODS: In this prospective study, 787 YT2D patients (diagnosed at &#x2264; age 40) were followed for a median of 5.7&#x2009;years. DKD progression was defined as an annual decline in estimated glomerular filtration rate (eGFR) of &#x2265;3&#x2009;mL/min/1.73&#x2009;m2 or&#x2009;&#x2265;&#x2009;40% reduction in eGFR from baseline. Plasma metabolites were measured by nuclear magnetic resonance spectroscopy. Multivariable regression analysis was performed in a discovery (N&#x2009;=&#x2009;550) and internal validation cohort (N&#x2009;=&#x2009;237). Integrative metabolomic-proteomic analysis (N&#x2009;=&#x2009;428) was performed using sparse partial least squares discriminant analysis (sPLS-DA). RESULTS: Ninety-eight metabolites were differentially expressed between DKD progressors and non-progressors, of which total branched-chain amino acids (BCAAs) (OR&#x2009;=&#x2009;0.60, 95% CI 0.46-0.79), valine (OR&#x2009;=&#x2009;0.62, 95% CI 0.48-0.81), and leucine (OR&#x2009;=&#x2009;0.56, 95% CI 0.43-0.74) associated with DKD progression, independent of metabolic risk factors. Integrative analysis identified three components comprising 23 proteins and 30 metabolites, involved in the citrate cycle and apoptosis, which improved prediction of DKD progression beyond clinical risk factors (AUC 0.69-0.83). CONCLUSION: Lower plasma BCAA levels are independently associated with DKD progression in YT2D. Integrative multi-omics analysis highlights disruptions in metabolic and apoptotic pathways, providing insights into DKD pathophysiology and potential biomarkers for early risk stratification.

Humans↗

Proteomic analysis of integral plasma membrane proteins.

Efficient methods for profiling proteins integral to the plasma membrane are highly desirable for the identification of overexpressed proteins in disease cells. Such methods will aid in both understanding basic biological processes and discovering protein targets for the design of therapeutic monoclonal antibodies. Avoiding contamination by subcellular organelles and cytosolic proteins is crucial to the successful proteomic analysis of integral plasma membrane proteins. Here we report a biotin-directed affinity purification (BDAP) method for the preparation of integral plasma membrane proteins, which involves (1) biotinylation of cell surface membrane proteins in viable cells, (2) affinity enrichment using streptavidin beads, and (3) depletion of plasma membrane-associated cytosolic proteins by harsh washes with high-salt and high-pH buffers. The integral plasma membrane proteins are then extracted and subjected to SDS-PAGE separation and HPLC/MS/MS for protein identification. We used the BDAP method to prepare integral plasma membrane proteins from a human lung cancer cell line. Western blotting analysis showed that the preparation was almost completely devoid of actin, a major cytosolic protein. Nano-HPLC/MS/MS analysis of only 30 microg of protein extracted from the affinity-enriched integral plasma membrane preparation led to the identification of 898 unique proteins, of which 781 were annotated with regard to their plasma membrane localization. Among the annotated proteins, at least 526 (67.3%) were integral plasma membrane proteins. Notable among them were 62 prenylated proteins and 45 Ras family proteins. To our knowledge, this is the most comprehensive proteomic analysis of integral plasma membrane proteins in mammalian cells to date. Given the importance of integral membrane proteins for drug design, the described approach will expedite the characterization of plasma membrane subproteomes and the discovery of plasma membrane protein drug targets.

Cell Line, Tumor↗

Integrating cytomics and proteomics.

Systems biology along with what is now classified as cytomics provides an excellent opportunity for cytometry to become integrated into studies where identification of functional proteins in complex cellular mixtures is desired. The combination of cell sorting with rapid protein-profiling platforms offers an automated and rapid technique for greater clarity, accuracy, and efficiency in identification of protein expression differences in mixed cell populations. The integration of cell sorting to purify cell populations opens up a new area for proteomic analysis. This article outlines an approach in which well defined cell analysis and separation tools are integrated into the proteomic programs within a core laboratory. In addition we introduce the concepts of flow cytometry sorting to demonstrate the importance of being able to use flow cytometry as a cell separation technology to identify and collect purified cell populations. Data demonstrating the speed and versatility of this combination of flow cytometry-based cell separation and protein separation and subsequent analysis, examples of protein maps from purified sorted cells, and an analysis of the overall procedure will be shown. It is clear that the power of cell sorting to separate heterogeneous populations of cells using specific phenotypic characteristics increases the power of rapid automated protein separation technologies.

Animals↗

Integrated genomic and proteomic analyses of a systematically perturbed metabolic network.

We demonstrate an integrated approach to build, test, and refine a model of a cellular pathway, in which perturbations to critical pathway components are analyzed using DNA microarrays, quantitative proteomics, and databases of known physical interactions. Using this approach, we identify 997 messenger RNAs responding to 20 systematic perturbations of the yeast galactose-utilization pathway, provide evidence that approximately 15 of 289 detected proteins are regulated posttranscriptionally, and identify explicit physical interactions governing the cellular response to each perturbation. We refine the model through further iterations of perturbation and global measurements, suggesting hypotheses about the regulation of galactose utilization and physical interactions between this and a variety of other metabolic pathways.

Computational Biology↗

Integrated transcriptome and proteome data: the challenges ahead.

The recent availability of platform technologies for high throughput proteome analysis has led to the emergence of integrated messenger RNA and protein expression data. The Pearson correlation coefficients for these data range from 0.46 to 0.76. In these integrated studies, serial analyses of gene expression and DNA microarrays have been used to quantify the transcriptome, while proteome analysis has been based on two-dimensional gel electrophoresis, isotope coded affinity tags and multidimensional protein identification technology. This paper provides a comprehensive review of the analytical techniques used in these studies and explores the extent to which the choice of experimental methodology can bias the correlation or the ability to detect proteins.

Animals↗

Thylakoid membrane proteomics.

Proteomics seeks to monitor the global complement of proteins within a cell or organism and accompanying plasticity with respect to development and environment. The proteome is dynamic, the product of current and past gene expression, countless protein-protein interactions and selective proteolytic systems. Consequently the snapshot that a proteomic measurement yields must be integrated into proteome flux; the flow of nutrients and energy through the protein pathways that catalyze and drive life. The thylakoid membrane proteome poses many technical challenges for proteomics. Integral membrane proteins present awkward physico-chemical properties and the abundant photosynthetic machinery conceals much less abundant and no less important proteins such as channels and transporters that control the interaction of stroma and lumen. Discussed here are contrasting approaches to thylakoid proteomics; 'shotgun' techniques that provide throughput benefits by cleaving proteins into smaller more-manageable peptide chunks versus intact protein techniques that provide more detailed and accurate pictures. A two-dimensional chromatography system directly interfaced to electrospray-ionization mass spectrometry has allowed the direct visualization of large reaction-center proteins (up to 83 kDa) from both Photosystems 1 and 2 providing an attractive avenue for characterization of thylakoid membrane proteomes under different conditions because of the ability to resolve molecular heterogeneity resulting from post-translational modifications such as phosphorylation and oxidation. A high-resolution spectrum of Bacteriorhodopsin recorded to an accuracy of 8 ppm using Fourier-transform mass spectrometry demonstrates the first application of this technique to intact polytopic integral membrane proteins.

Journal Article↗

Statistically integrated metabonomic-proteomic studies on a human prostate cancer xenograft model in mice.

A novel statistically integrated proteometabonomic method has been developed and applied to a human tumor xenograft mouse model of prostate cancer. Parallel 2D-DIGE proteomic and 1H NMR metabolic profile data were collected on blood plasma from mice implanted with a prostate cancer (PC-3) xenograft and from matched control animals. To interpret the xenograft-induced differences in plasma profiles, multivariate statistical algorithms including orthogonal projection to latent structure (OPLS) were applied to generate models characterizing the disease profile. Two approaches to integrating metabonomic data matrices are presented based on OPLS algorithms to provide a framework for generating models relating to the specific and common sources of variation in the metabolite concentrations and protein abundances that can be directly related to the disease model. Multiple correlations between metabolites and proteins were found, including associations between serotransferrin precursor and both tyrosine and 3-D-hydroxybutyrate. Additionally, a correlation between decreased concentration of tyrosine and increased presence of gelsolin was also observed. This approach can provide enhanced recovery of combination candidate biomarkers across multi-omic platforms, thus, enhancing understanding of in vivo model systems studied by multiple omic technologies.

Animals↗

TNF&#x3b1;-induced endothelial extracellular vesicles regulate astrocyte function: an integrated transcriptomic and proteomic study.

Endothelial cells and astrocytes are critical structural and functional components of the blood-brain barrier. In many neuroinflammatory diseases, endothelial cells are among the first to respond to inflammatory stimuli and release extracellular vesicles (EVs). However, whether inflammatory stimulation alters EV RNA cargo and subsequently regulates astrocyte function remains unclear. In this study, we performed integrated RNA sequencing and proteomic analyses to investigate the effects of TNF&#x3b1;-stimulated endothelial EVs on astrocytes. RNA profiling revealed significant alterations in EV cargo after TNF&#x3b1; stimulation, including 867 upregulated and 577 downregulated mRNAs, 317 upregulated and 15 downregulated lncRNAs, and 88 upregulated and 62 downregulated miRNAs. The results of functional enrichment analysis suggested that altered EV RNAs may primarily promote inflammatory responses, cell migration, and RNA splicing in astrocytes while reducing their regulatory effects on neuronal projection and calcium homeostasis. Further integrative analysis of EV RNAs and astrocytic proteomics revealed key overlapping targets, including upregulated expression of ICAM1, SOD2, TFPI2, and TNFAIP8, whereas NFKBIA expression was consistently decreased. Network analysis revealed NF-&#x3ba;B as the central regulatory node. Reduced levels of EV-derived NFKBIA mRNA were associated with decreased I&#x3ba;B&#x3b1; protein levels in astrocytes, which promoted NF-&#x3ba;B activation and inflammatory cytokine release. Finally, overexpression of I&#x3ba;B&#x3b1; in astrocytes significantly attenuated TNF&#x3b1; EV-induced IL-1&#x3b2; and IL-6 secretion. Collectively, these findings demonstrate that TNF&#x3b1;-stimulated endothelial EVs coordinately regulate astrocyte function through mRNA, lncRNA, and miRNA cargo and that the I&#x3ba;B&#x3b1;/NF-&#x3ba;B axis may be a key mechanism underlying endothelial EV-mediated inflammatory disruption of the blood-brain barrier.

Astrocytes↗

A bioinformatics perspective on proteomics: data storage, analysis, and integration.

The field of proteomics is advancing rapidly as a result of powerful new technologies and proteomics experiments yield a vast and increasing amount of information. Data regarding protein occurrence, abundance, identity, sequence, structure, properties, and interactions need to be stored. Currently, a common standard has not yet been established and open access to results is needed for further development of robust analysis algorithms. Databases for proteomics will evolve from pure storage into knowledge resources, providing a repository for information (meta-data) which is mainly not stored in simple flat files. This review will shed light on recent steps towards the generation of a common standard in proteomics data storage and integration, but is not meant to be a comprehensive overview of all available databases and tools in the proteomics community.

Computational Biology↗

Integrated Transcriptomic and Proteomic Analysis Elucidates the Mechanisms of Huperzine A Injection Against Cerebral Ischemia/Reperfusion Injury.

BACKGROUND: After recanalization in acute ischemic stroke, cerebral ischemia/reperfusion injury (CI/RI) drives a cascade of pathophysiological events that worsen clinical outcomes, yet effective therapeutic options remain limited. Given the neuroprotective potential of Huperzine A (HupA), the efficacy of HupA injection (HAI, a major clinical formulation of HupA) against CI/RI and its underlying molecular basis warrant investigation. METHODS: In a mouse model of CI/RI, neurological performance, locomotor ability, cerebral infarction, histopathological alterations, and apoptotic neurons were jointly used to assess the anti-CI/RI effect of HAI at two different doses. An integrated transcriptomic and proteomic strategy was adopted to decipher the key anti-CI/RI mechanisms of HAI and then validated experimentally. RESULTS: Compared with vehicle&#x2011;treated CI/RI mice, HAI intervention significantly alleviated neurobehavioral deficits, decreased infarct size, mitigated histopathological damage, and suppressed neuronal apoptosis (P < 0.01). Both separate and combined transcriptomic and proteomic analyses highlighted that the complement and coagulation cascades, together with inflammation, were strongly correlated with HAI's beneficial action in preventing CI/RI. Indeed, HAI treatment effectively normalized the dysregulated mRNA and protein levels of pivotal targets within the complement and coagulation cascades, including C3, C5, C9, CFB, MASP2, F7, F10, F12, and SERPINE1, in the damaged cortical tissues of CI/RI mice (P < 0.05). Moreover, this intervention markedly attenuated the abnormally elevated expression of multiple inflammatory mediators, including TLR2, TLR4, TNF-&#x3b1;, IL-1&#x3b2;, IL-6, CCL2, CCL5, CXCL1, ICAM1, S100A9, LCN2, MMP8, and MMP9, at both the mRNA and protein levels (P < 0.01). CONCLUSION: Collectively, our data suggest that HAI may confer efficacy against CI/RI by modulating the complement and coagulation cascades and orchestrating the inflammatory response. Although further investigation is warranted, these preliminary findings provide a foundation for accelerating the clinical translation of HAI as a novel neuroprotectant against CI/RI in ischemic stroke.

Animals↗

Data merging for integrated microarray and proteomic analysis.

The functioning of even a simple biological system is much more complicated than the sum of its genes, proteins and metabolites. A premise of systems biology is that molecular profiling will facilitate the discovery and characterization of important disease pathways. However, as multiple levels of effector pathway regulation appear to be the norm rather than the exception, a significant challenge presented by high-throughput genomics and proteomics technologies is the extraction of the biological implications of complex data. Thus, integration of heterogeneous types of data generated from diverse global technology platforms represents the first challenge in developing the necessary foundational databases needed for predictive modelling of cell and tissue responses. Given the apparent difficulty in defining the correspondence between gene expression and protein abundance measured in several systems to date, how do we make sense of these data and design the next experiment? In this review, we highlight current approaches and challenges associated with integration and analysis of heterogeneous data sets, focusing on global analysis obtained from high-throughput technologies.

Animals↗

Integrated Genomic and Proteomic Analysis Reveals T-B Lymphocyte Signatures in the MYCN Driven "Immune Desert" of Specific Neuroblastoma Subtypes.

AIMS: This study aims to systematically dissect how MYCN amplification shapes the immunosuppressive tumor microenvironment (TME) in high-risk neuroblastoma, elucidating key mechanisms underlying immune evasion. METHODS: We performed an integrated multi-omics analysis of bulk RNA-seq (n&#x2009;=&#x2009;721), single-cell RNA-seq (n&#x2009;=&#x2009;9), proteomic data (n&#x2009;=&#x2009;49) and spatial transcriptomics (Visium, with external validation in melanoma). Analyses included unsupervised clustering, cell-cell communication inference, transcriptional regulatory network reconstruction, and spatial proximity assessment to map the immune landscape. RESULTS: A distinct molecular subtype (Class C), defined by MYCN amplification and poor prognosis, exhibited a comprehensive "immune desert" phenotype characterized by low immune scores and minimal leukocyte infiltration. Single-cell analysis confirmed significant depletion of T and B lymphocytes within the Class C TME. Dysregulated transcriptional networks were identified, including upregulation of REL and EOMES in T cells-with EOMES potentially driving exhaustion via regulation of Transient Receptor Potential (TRP) genes, and REL inhibition enhancing cytotoxic function in&#xa0;vitro. A unique immunosuppressive B-cell subset (B7) engaged in enhanced crosstalk with exhausted T cells and harbored a MYC-centered network linked to cell cycle dysregulation and poor survival. Spatial transcriptomics revealed significant proximity between B7-active regions and Treg/exhaustion-enriched areas, externally validated in melanoma. Proteomic data validated elevated REL expression in MYCN-amplified tumors. CONCLUSION: This work delineates the immunosuppressive architecture of MYCN-driven neuroblastoma, revealing novel regulatory nodes within specific lymphocyte compartments. Integrating single-cell, spatial, and proteomic evidence, we propose REL inhibition as a therapeutic candidate, the EOMES/TRP axis as a bioinformatically supported hypothesis, and the B7/MYC hub as a hypothesis supported by transcriptomic and spatial evidence.

Humans↗

Integrative genomic and proteomic analysis of prostate cancer reveals signatures of metastatic progression.

Molecular profiling of cancer at the transcript level has become routine. Large-scale analysis of proteomic alterations during cancer progression has been a more daunting task. Here, we employed high-throughput immunoblotting in order to interrogate tissue extracts derived from prostate cancer. We identified 64 proteins that were altered in prostate cancer relative to benign prostate and 156 additional proteins that were altered in metastatic disease. An integrative analysis of this compendium of proteomic alterations and transcriptomic data was performed, revealing only 48%-64% concordance between protein and transcript levels. Importantly, differential proteomic alterations between metastatic and clinically localized prostate cancer that mapped concordantly to gene transcripts served as predictors of clinical outcome in prostate cancer as well as other solid tumors.

Disease Progression↗