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PubMedDate not supplied

Quantitative profiling of the detergent-resistant membrane proteome of iota-b toxin induced vero cells.

Enzyme-mediated 18O/16O differential labeling of proteome samples often suffers from incomplete exchange of the carboxy-terminus oxygen atoms, resulting in ambiguity in the measurable abundance differences. In this study, an 18O/16O labeling strategy was optimized for and applied to the solution-based comparative analysis of the detergent-resistant membrane proteome (DRMP) of untreated and Iota-b (Ib)-induced Vero cells. Solubilization and tryptic digestion of the DRMP was conducted in a buffer containing 60% methanol. Unfortunately, the activity of trypsin is attenuated at this methanol concentration hampering the ability to obtain complete oxygen atom turnover. Therefore, the incorporation of the 18O atoms was decoupled from the protein digestion step by carrying out the trypsin-mediated heavy atom incorporation in a buffer containing 20% methanol; a concentration at which trypsin activity is enhanced compared to purely aqueous conditions. After isotopic labeling, the samples were combined, fractionated by strong cation exchange and analyzed by microcapillary reversed-phase liquid chromatography coupled on-line with electrospray ionization tandem mass spectrometry. In total, over 1400 unique peptides, corresponding to almost 600 proteins, were identified and quantitated, including all known caveolar and lipid raft marker proteins. The quantitative profiling of Ib-induced DRMP from Vero cells revealed several proteins with altered expression levels suggesting their possible role in Ib binding/uptake.

ADP Ribose Transferases↗

PubMed2025

Prior vaccination prevents overactivation of innate immune responses during COVID-19 breakthrough infection.

At this stage in the COVID-19 pandemic, most infections are "breakthrough" infections that occur in individuals with prior severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure. To refine long-term vaccine strategies against emerging variants, we examined both innate and adaptive immunity in breakthrough infections. We performed single-cell transcriptomic, proteomic, and functional profiling of primary and breakthrough infections to compare immune responses from unvaccinated and vaccinated individuals during the SARS-CoV-2 Delta wave. Breakthrough infections were characterized by a less activated transcriptomic profile in monocytes and natural killer cells, with induction of pathways limiting monocyte migratory potential and natural killer cell proliferation. Furthermore, we observed a female-specific increase in transcriptomic and proteomic activation of multiple innate immune cell subsets during breakthrough infections. These insights suggest that prior SARS-CoV-2 vaccination prevents overactivation of innate immune responses during breakthrough infections with discernible sex-specific patterns and underscore the potential of harnessing vaccines in mitigating pathologic immune responses resulting from overactivation.

Immunity, Innate↗

PubMedDate not supplied

Multidimensional protein profiling technology and its application to human plasma proteome.

In clinical and diagnostic proteomics, it is essential to develop a comprehensive and robust system for proteome analysis. Although multidimensional liquid chromatography/tandem mass spectrometry (LC/MS/MS) systems have been recently developed as powerful tools especially for identification of protein complexes, these systems still some drawbacks in their application to clinical research that requires an analysis of a large number of human samples. Therefore, in this study, we have constructed a technically simple and high throughput protein profiling system comprising a two-dimensional (2D)-LC/MS/MS system which integrates both a strong cation exchange (SCX) chromatography and a microLC/MS/MS system with micro-flowing reversed-phase chromatography. Using the microLC/MS/MS system as the second dimensional chromatography, SCX separation has been optimized as an off-line first dimensional peptide fractionation. To evaluate the performance of the constructed 2D-LC/MS/MS system, the results of detection and identification of proteins were compared using digests mixtures of 6 authentic proteins with those obtained using one-dimensional microLC/MS/MS system. The number of peptide fragments detected and the coverage of protein sequence were found to be more than double through the use of our newly built 2D-LC/MS/MS system. Furthermore, this multidimensional protein profiling system has been applied to plasma proteome in order to examine its feasibility for clinical proteomics. The experimental results revealed the identification of 174 proteins from one serum sample depleted HSA and IgG which corresponds to only 1 microL of plasma, and the total analysis run time was less than half a day, indicating a fairly high possibility of practicing clinical proteomics in a high throughput manner.

Blood Proteins↗

PubMed2004

Proteomics in nephrology: current status and future directions.

Proteomics is one among various 'OMICS' fields that have been growing rapidly in the postgenomic era. During the past few years, proteomics has been extensively applied to several fields of medicine to better understand normal physiology, to define the pathophysiology of diseases, and to identify novel biomarkers and new therapeutic targets. This review focuses on current status and future directions of proteomics in the nephrology field. Recent studies of renal proteome, proteomes of individual intrarenal structures (i.e., glomerular, vascular, tubular, brush border membrane, mesangial, and podocyte proteomes), urinary proteome, and protein profiles in dialysate or ultrafiltrate removed by renal replacement therapy are summarized.

Animals↗

PubMed2005

Low molecular weight proteomic information distinguishes metastatic from benign pheochromocytoma.

Metastatic lesions occur in up to 36% of patients with pheochromocytoma. Currently there is no way to reliably detect or predict which patients are at risk for metastatic pheochromocytoma. Thus, the discovery of biomarkers that could distinguish patients with benign disease from those with metastatic disease would be of great clinical value. Using surface-enhanced laser desorption ionization protein chips combined with high-resolution mass spectrometry, we tested the hypothesis that pheochromocytoma pathologic states can be reflected as biomarker information within the low molecular weight (LMW) region of the serum proteome. LMW protein profiles were generated from the serum of 67 pheochromocytoma patients from four institutions and analyzed by two different bioinformatics approaches employing pattern recognition algorithms to determine if the LMW component of the circulatory proteome contains potentially useful discriminatory information. Both approaches were able to identify combinations of LMW molecules which could distinguish all metastatic from all benign pheochromocytomas in a separate blinded validation set. In conclusion, for this study set low molecular mass biomarker information correlated with pheochromocytoma pathologic state using blinded validation. If confirmed in larger validation studies, efforts to identify the underlying diagnostic molecules by sequencing would be warranted. In the future, measurement of these biomarkers could be potentially used to improve the ability to identify patients with metastatic disease.

Adolescent↗

PubMed2024

Cross-platform proteomics signatures of extreme old age.

In previous work, we used a SomaLogic platform targeting approximately 5000 proteins to generate a serum protein signature of centenarians that we validated in independent studies that used the same technology. We set here to validate and possibly expand the results by profiling the serum proteome of a subset of individuals included in the original study using liquid chromatography tandem mass spectrometry (LC-MS/MS). Following pre-processing, the LC-MS/MS data provided quantification of 398 proteins, with only 266 proteins shared by both platforms. At 1% FDR statistical significance threshold, the analysis of LC-MS/MS data detected 44 proteins associated with extreme old age, including 23 of the original analysis. To identify proteins for which associations between expression and extreme-old age were conserved across platforms, we performed inter-study conservation testing of the 266 proteins quantified by both platforms using a method that accounts for the correlation between the results. From these tests, a total of 80 proteins reached 5% FDR statistical significance, and 26 of these proteins had concordant pattern of gene expression in whole blood generated in an independent set. This signature of 80 proteins points to blood coagulation, IGF signaling, extracellular matrix (ECM) organization, and complement cascade as important pathways whose protein level changes provide evidence for age-related adjustments that distinguish centenarians from younger individuals. The comparison with blood transcriptomics also highlights a possible role for neutrophil degranulation in aging.

Humans↗

PubMed2026

Omics signature of new-onset mild cognitive impairment and dementia in a population-based study.

Plasma proteomics and metabolomics snapshots reveal a molecular signature in circulation delineating pathophysiology of major and minor neurocognitive disorder. To identify new cues to disease aetiology and diagnostic approach, we applied plasma proteomics and metabolomics profiling platforms to samples collected in a population-based study of the Singapore Longitudinal Ageing Studies Wave 2 (SLAS-2). In this longitudinal study, blood samples were analysed with standard clinical chemistry, plasma proteomics (Sengenics) and metabolomics (Nightingale) panels. Participants were followed up for the development of mild cognitive impairment (MCI) and dementia for 3-5 years. Of the total 1,892 molecules in all assay types, 463 demonstrated significant associations with baseline prevalent MCI and dementia. We trained an automatic linear modelling of predictors for follow-up new-onset MCI and dementia. The best model consists of 10 variables including ZSCAN18, PRKD3, SPANXN4, DDX43, saturated fatty acids, PPP3CA, NFATC4, IL-8, PAK6, and PDGFB. In terms of molecular function, these molecular markers are involved in immunological dysfunction and inflammatory reaction, protein coding, lipids, DNA-binding transcription factor activity, and nervous system development. In conclusion, our current research has identified an omics signature linked to new-onset mild cognitive disorder and dementia, which we hope can help enhance the accuracy of their diagnosis using circulating blood samples.

Humans↗

PubMed2026

Proteomic and Phosphoproteomic Signatures Link Molecular Remodeling to Behavioral Outcomes Following Elderberry and DHA Supplementation in Aging Mice.

Background: Aging is a risk factor for Alzheimer's disease and related dementias, which are associated with synaptic dysfunction and cognitive decline. Elderberry (Sambucus spp.) is rich in anthocyanins with antioxidant and anti-inflammatory properties. Docosahexaenoic acid (DHA), an essential fatty acid, plays a key role in neuronal membrane integrity during brain aging. However, it remains unclear whether elderberry and DHA exert overlapping or distinct effects on brain aging and how these relate to molecular signaling. This study aimed to characterize molecular signatures induced by dietary supplementation and to determine their relationships with behavioral outcomes. Methods: 44-week-old male C57BL/6J mice were randomly assigned to control, elderberry, DHA, or combined diets for 12 weeks. Behavioral testing assessed anxiety-like behavior, spatial learning and memory. Brain tissues underwent proteomic and phosphoproteomic profiling and fatty-acid analysis. Data were analyzed using Ingenuity Pathway Analysis to identify enriched pathways, upstream regulators, and functional associations. Results: Elderberry as well as DHA supplementation induced targeted remodeling of the proteome and phosphoproteome, with pathway enrichment involving synaptogenesis, glutamatergic signaling, and long-term potentiation. Upstream-regulator analysis predicted elderberry-associated CDK5 signaling, accompanied by reduced MAPT/Tau phosphorylation at selected sites, whereas DHA supplementation was associated with CAMK-related signaling. DHA supplementation altered fatty-acid composition, increasing the n-3/n-6 ratio. Elderberry reduced anxiety-like behavior and improved target-directed search during the Barnes maze probe test. Molecular signatures were examined in relation to the measured behavioral outcomes. Conclusions: Elderberry and DHA are associated with distinct molecular networks related to synaptic function and behavioral outcomes in the aging male mouse brain. These findings support further investigation of elderberry and DHA as dietary interventions targeting molecular and behavioral features of brain aging.

Animals↗

PubMed2005

Comparison of proteome and antigenic proteome between two Neospora caninum isolates.

This study was conducted to explore the relationship between two isolates of Neospora caninum (N. caninum) (KBA-2 and VMDL-1) using proteomics. To achieve the goal, proteins of N. caninum tachyzoite lysates of KBA-2 and VMDL-1 were separated by two-dimensional gel electrophoresis (2-DE), stained with silver-nitrate and analyzed using matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) to compare protein profiles. In addition, proteins separated by 2-DE were transferred to membranes, probed with bovine anti-N. caninum KBA-2 immunoglobulin G, and reactive proteins were visualized and compared between the two isolates. Most spots on 2-DE profiles and antigenic spots on 2-DE immunoblot profiles were located at similar locations in terms of isoelectric point and molecular weight. Proteins common to both isolates included the following: heat shock protein 70, subtilisin-like serine protease, nucleoside triphosphatase, heat shock protein 60, pyruvate kinase, tubulin alpha, tubulin beta, enolase, putative protein disulfide isomerase, actin, fructase-1,6-bisphosphatase, putative ribosomal protein S2, microneme protein Nc-P38, lactate dihydrogenase, fructose-1,6-bisphosphatase aldolase, serine threonine phosphatase 2C, 14-3-3 protein homologue, N. caninum dense granule-1 and NcGRA2. As a consequence, even though N. caninum KBA-2 and VMDL-1 isolates were isolated from geographically distinct locations there were significant homology in the proteome and antigenic proteome profiles. In addition, proteomic approach was verified as a useful tool for understanding of host immune response against different isolates of protozoa.

Animals↗

PubMed2004

Protein profiling of cervical cancer by protein-biochips: proteomic scoring to discriminate cervical cancer from normal cervix.

Analysis of multiple proteins is thought to be essential for establishment of signature proteomic patterns that may distinguish cancer from non-cancer. Surface-enhanced laser desorption/ionization (SELDI) is an affinity-based mass spectrometric method in which proteins of interest are selectively absorbed to a chemically modified surface on a biochip. This technology may provide protein profiling of a variety of biological specimens. In this study, we explored whether the protein biochip SELDI approach could differentiate cervical cancer from non-cancer cohorts. We screened protein profiles generated by SELDI in 62 cervical epithelial cell samples microdissected from 35 invasive cervical cancer and 27 age-matched normal cervix tissue specimens, respectively. The cell lysates of pure populations of cervical cells were applied onto Ciphergen ProteinChip WCX2 Arrays. Proteins bound to the chips were analyzed on a ProteinChip Reader Model PBS II. Derived proteomic patterns were converted to a simple proteomic scoring for distinguishing cancer from non-cancer cohorts. SELDI protein profiles of cell lysates from 20 cervical cancer and 15 normal cervix tissue specimens were used to train and develop a classification scoring system that used a seven-protein mass pattern. The training samples could be correctly discriminated. When a test set of 27 samples was used for evaluation of this scoring system to distinguish cervical cancer from non-cancer, a sensitivity of 87%, a specificity of 100%, a positive predictive value of 100%, and a negative predictive value of 86% for the test population were obtained. All seven proteins appeared to be down regulated in cervical cancer. The results from this study indicate that the proteomics approach of SELDI mass spectrometry, in combination with a simple scoring system, may distinguish cervical cancer from its normal counterpart. If this approach is also workable in the analysis of cervical exfoliated cell lysate, it might potentially be used in the early diagnosis of invasive cervical cancer. In addition, the identification of these specific proteins in cervical cancer may also facilitate the discovery of new cervical tumor marker(s).

Adult↗

PubMed2002

Application of metabolomics to plant genotype discrimination using statistics and machine learning.

MOTIVATION: Metabolomics is a post genomic technology which seeks to provide a comprehensive profile of all the metabolites present in a biological sample. This complements the mRNA profiles provided by microarrays, and the protein profiles provided by proteomics. To test the power of metabolome analysis we selected the problem of discrimating between related genotypes of Arabidopsis. Specifically, the problem tackled was to discrimate between two background genotypes (Col0 and C24) and, more significantly, the offspring produced by the crossbreeding of these two lines, the progeny (whose genotypes would differ only in their maternally inherited mitichondia and chloroplasts). OVERVIEW: A gas chromotography--mass spectrometry (GCMS) profiling protocol was used to identify 433 metabolites in the samples. The metabolomic profiles were compared using descriptive statistics which indicated that key primary metabolites vary more than other metabolites. We then applied neural networks to discriminate between the genotypes. This showed clearly that the two background lines can be discrimated between each other and their progeny, and indicated that the two progeny lines can also be discriminated. We applied Euclidean hierarchical and Principal Component Analysis (PCA) to help understand the basis of genotype discrimination. PCA indicated that malic acid and citrate are the two most important metabolites for discriminating between the background lines, and glucose and fructose are two most important metabolites for discriminating between the crosses. These results are consistant with genotype differences in mitochondia and chloroplasts.

Algorithms↗

PubMed2026

Integrated multi-omics profiling of amniotic fluid identifies predictive biomarkers for fetal growth restriction trajectories.

BACKGROUND: Fetal growth restriction (FGR) is a complex condition with highly heterogeneous clinical outcomes, making prenatal distinction between transient and persistent growth failure challenging. This study aims to identify amniotic fluid (AF) biomarkers capable of differentiating distinct FGR trajectories and characterizing persistent growth failure mechanisms. METHODS: Integrated proteomic and metabolomic profiling was performed on AF samples from transient FGR (n = 11), persistent FGR (n = 9), and healthy controls (n = 13). Diagnostic and prognostic models were developed using multivariate analysis. Selected protein candidates were validated via ELISA in an independent cohort (n = 69). RESULTS: Multi-omics analysis revealed distinct molecular signatures for FGR stratification. A two-protein diagnostic panel (PDGFA and phospho-STAT5A) achieved an AUC of 1.000 in the discovery stage and 0.780 in the external validation cohort. For prognostic assessment, a molecular signature including IREB2, HLA-C, and PLXNB2 accurately predicted persistent growth failure from transient recovery (AUC = 0.966). Cross-platform integration highlighted the mass spectrometry-derived WASHC2C as a central hub protein with a significant progressive increase across the control, transient, and persistent groups (p < 0.001). CONCLUSIONS: This study establishes a multi-omics framework for prenatal FGR stratification. Our findings identify distinct molecular signatures reflecting the intrauterine environment and provide high-performance molecular tools for predicting divergent fetal growth trajectories to guide personalized clinical decision-making.

Humans↗

PubMed2006

Using size exclusion chromatography-RPLC and RPLC-CIEF as two-dimensional separation strategies for protein profiling.

Bottom-up proteomics (analyzing peptides that result from protein digestion) has demonstrated capability for broad proteome coverage and good throughput. However, due to incomplete sequence coverage, this approach is not ideally suited to the study of modified proteins. The modification complement of a protein can best be elucidated by analyzing the intact protein. 2-DE, typically coupled with the analysis of peptides that result from in-gel digestion, is the most frequently applied protein separation technique in MS-based proteomics. As an alternative, numerous column-based liquid phase techniques, which are generally more amenable to automation, are being investigated. In this work, the combination of size-exclusion chromatography (SEC) fractionation with RPLC-Fourier-transform ion cyclotron resonance (FTICR)-MS is compared with the combination of RPLC fractionation with CIEF-FTICR-MS for the analysis of the Shewanella oneidensis proteome. SEC-RPLC-FTICR-MS allowed the detection of 297 proteins, as opposed to 166 using RPLC-CIEF-FTICR-MS, indicating that approaches based on LC-MS provide better coverage. However, there were significant differences in the sets of proteins detected and both approaches provide a basis for accurately quantifying changes in protein and modified protein abundances.

Bacterial Proteins↗

PubMed2005

Engineering HlyA hypersecretion in Escherichia coli based on proteomic and microarray analyses.

Escherichia coli is a common host for recombinant protein production for biotechnology applications. Secretion to the extracellular media has the potential to reduce protein aggregation and to simplify downstream purification. However, the complexity of the mechanisms of protein secretion has confounded prior attempts to engineer enhanced secretion phenotypes. Here, mutagenesis was used to perturb E. coli W3110 cells secreting HlyA via a Type I pathway. An activity assay identified a mutant secreting fourfold more active alpha-hemolysin than the parent strain. The mutant was characterized using both high-density microarrays for mRNA profiling and a proteomics strategy for protein expression. The relative mRNA and protein expression levels of tRNA-synthetases were decreased in the mutant compared to the parent. A mathematical model of prokaryotic translation was used to design a variant of the hlyA gene that encodes the same amino acid sequence but uses rare codons to slow the rate of translation by altering five bases. Analysis of the parent strain transformed with a plasmid containing this variant gene resulted in the recovery of, and further improvement upon, the selected hypersecretion phenotype. These results present one of the first successful metabolic engineering attempts based on molecular information provided by mRNA and protein expression profiling approaches and resulting in a phenotype useful to the biotechnology community.

Computer Simulation↗

PubMed2002

Integrating cancer genomics and proteomics in the post-genome era.

The dawn of the post-genome era is leading to extraordinary opportunities in biomedicine. Our group has embarked on a major effort to integrate genomics, transcriptomics and proteomics for the profiling of tumor tissues, an approach we refer to as operomics. Our major goals are the molecular classification of tumors and the identification of markers for the early detection of cancer. Molecular analyses of tumors rely on microdissected tissues, which are simultaneously investigated for genomic, transcriptomic and proteomic changes. Genomic alterations in tumor cells being investigated include deletions, amplifications and methylation changes across the entire genome as well as point mutations in specific genes. Expression analysis at the RNA level is being undertaken using oligonucleotide and cDNA based microarrays. An important aspect of our approach is the large-scale identification and quantitative analysis of tumor proteins in whole cell lysates as well as in protein compartments. Protein separation strategies include two-dimensional polyacrylamide gel electrophoresis and liquid chromatography. Specific protein subsets, of interest include membrane proteins, secreted proteins and antigenic proteins as sources of biomarkers for early detection of cancer. Our current approach is illustrated with findings stemming from our studies of human gliomas.

Brain Neoplasms↗

PubMed2006

Altered protein profile in chronic myeloid leukemia chronic phase identified by a comparative proteomic study.

Chronic myeloid leukemia is a hematological disorder in which the Ph chromosome is a marker of the disease, detected virtually in all cases. The chimeric transcripts encode a 210-kDa chimeric protein with altered tyrosine kinase activity, responsible for the disease phenotype. In this work, we tried to identify which are the molecular changes common to chronic phase patients, those that represent the chronic phase molecular phenotype. To address this problem we analyzed through a comparative proteomic approach, several CML bone marrow cells protein profile from patients in chronic phase and healthy bone marrow donors. From these results, we identified 31 differentially expressed proteins. Among these proteins, we pointed out c-Myc binding protein 1, 53BP1, Mdm4, OSBP-related protein 3 and Mortalin as putative candidates to BCR-ABL targets in chronic phase. Moreover, we describe for the first time the cytoplasmic protein map from bone marrow cells that helped in the elucidation of the changes we were looking for.

Adult↗

PubMed2006

Genomic approach to biomarker identification and its recent applications.

This paper discusses selected activities, issues, and challenges in recent development of analytical methods and applications in biomarker identification and validation using state-of-the-art genomic approaches. Molecular profiling via genomics, proteomics, and metabonomics has opened new windows to study disease states and biological systems. It has also provided exciting opportunities for novel applications in clinical research as well as in drug discovery and development. In the past several years, we have witnessed enormous progress resulting particularly from gene expression profiling of mRNA or transcriptomics. After a brief review on technology advances in gene expression profiling using microarrays, I mainly discuss recent developments of the genomic approaches to biomarker identification and validation in two major types of applications. The first type involves examples in cancer diagnostics and prognostics based on tumor gene expression profiling, whereas the second type involves biomarker applications in drug discovery and development. The focus will be on analytical methods and algorithms that have been developed in recent years facilitating biomarker discovery and application by leveraging genome-wide expression profiles derived from microarrays. Technical issues in experimental design, data processing, error modeling, quality control, figures of merit for performance evaluation, and meta-analysis related to biomarker discovery and application are also discussed. A case study of disease outcome prognosis for breast cancer patients based on tumor expression pattern is presented before closing remarks.

Biomarkers↗

PubMed2006

Activity profiling of deubiquitinating enzymes in cervical carcinoma biopsies and cell lines.

Ubiquitin specific proteases (USPs) regulate the production and recycling of ubiquitin and are thereby critically involved in the control of cell growth, differentiation, and apoptosis. Increasing evidence implicates deregulation of USPs in malignant transformation but there is very little information on the overall and specific activity of USPs in normal and tumor tissues. We have used a chemistry-based functional proteomics approach to profile the activities of individual USPs in biopsies of human papillomavirus (HPV) carrying cervical carcinoma and adjacent normal tissue. To assess the contribution of HPV proteins, USP activity was also compared in HPV positive and negative cervical carcinoma cell lines and HPV E6/E7 immortalized human keratinocytes. The activity of the C-terminal hydrolases UCH-L3 and UCH37 was upregulated in the majority of tumor tissues compared to the adjacent normal tissues. UCH-L1 activity was lower in a significant proportion of the tumors but to a less extent in advanced tumors. In accordance with the relatively low UCH-L1 activity in tumor biopsies, UCH-L1 was detected only in one out of eight cervical carcinoma lines. UCH-L1, UCH-L3, USP7, and USP9X activity was upregulated following HPV E6/E7 immortalization of keratinocytes, suggesting a role of these enzymes in growth transformation.

Biopsy↗