Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “PROTEOMICS”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

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

At least 1,225 records · Page 68Linked to original sources
PubMed2005

Evaluation of "shotgun" proteomics for identification of biological threat agents in complex environmental matrixes: experimental simulations.

There is currently a great need for rapid detection and positive identification of biological threat agents, as well as microbial species in general, directly from complex environmental samples. This need is most urgent in the area of homeland security, but also extends into medical, environmental, and agricultural sciences. Mass-spectrometry-based analysis is one of the leading technologies in the field with a diversity of different methodologies for biothreat detection. Over the past few years, "shotgun"proteomics has become one method of choice for the rapid analysis of complex protein mixtures by mass spectrometry. Recently, it was demonstrated that this methodology is capable of distinguishing a target species against a large database of background species from a single-component sample or dual-component mixtures with relatively the same concentration. Here, we examine the potential of shotgun proteomics to analyze a target species in a background of four contaminant species. We tested the capability of a common commercial mass-spectrometry-based shotgun proteomics platform for the detection of the target species (Escherichia coli) at four different concentrations and four different time points of analysis. We also tested the effect of database size on positive identification of the four microbes used in this study by testing a small (13-species) database and a large (261-species) database. The results clearly indicated that this technology could easily identify the target species at 20% in the background mixture at a 60, 120, 180, or 240 min analysis time with the small database. The results also indicated that the target species could easily be identified at 20% or 6% but could not be identified at 0.6% or 0.06% in either a 240 min analysis or a 30 h analysis with the small database. The effects of the large database were severe on the target species where detection above the background at any concentration used in this study was impossible, though the three other microbes used in this study were clearly identified above the background when analyzed with the large database. This study points to the potential application of this technology for biological threat agent detection but highlights many areas of needed research before the technology will be useful in real world samples.

Biological Warfare↗
PubMed2004

High-throughput comparative proteome analysis using a quantitative cysteinyl-peptide enrichment technology.

A new quantitative cysteinyl-peptide enrichment technology (QCET) was developed to achieve higher efficiency, greater dynamic range, and higher throughput in quantitative proteomics that use stable-isotope labeling techniques combined with high-resolution liquid chromatography (LC)-mass spectrometry (MS). This approach involves (18)O labeling of tryptic peptides, high-efficiency enrichment of cysteine-containing peptides, and confident protein identification and quantification using the accurate mass and time tag strategy. Proteome profiling of naïve and in vitro-differentiated human mammary epithelial cells using QCET resulted in the identification and quantification of 603 proteins in a single LC-Fourier transform ion cyclotron resonance MS analysis. Advantages of this technology include the following: (1) a simple, highly efficient method for enriching cysteinyl-peptides; (2) a high-throughput strategy suitable for extensive proteome analysis; and (3) improved labeling efficiency for better quantitative measurements. This technology enhances both the functional analysis of biological systems and the detection of potential clinical biomarkers.

Cells, Cultured↗
PubMed2005

Sample handling for mass spectrometric proteomic investigations of human sera.

Proteomic investigations of sera are potentially of value for diagnosis, prognosis, choice of therapy, and disease activity assessment by virtue of discovering new biomarkers and biomarker patterns. Much debate focuses on the biological relevance and the need for identification of such biomarkers while less effort has been invested in devising standard procedures for sample preparation and storage in relation to model building based on complex sets of mass spectrometric (MS) data. Thus, development of standardized methods for collection and storage of patient samples together with standards for transportation and handling of samples are needed. This requires knowledge about how sample processing affects MS-based proteome analyses and thereby how nonbiological biased classification errors are avoided. In this study, we characterize the effects of sample handling, including clotting conditions, storage temperature, storage time, and freeze/thaw cycles, on MS-based proteomics of human serum by using principal components analysis, support vector machine learning, and clustering methods based on genetic algorithms as class modeling and prediction methods. Using spiking to artificially create differentiable sample groups, this integrated approach yields data that--even when working with sample groups that differ more than may be expected in biological studies--clearly demonstrate the need for comparable sampling conditions for samples used for modeling and for the samples that are going into the test set group. Also, the study emphasizes the difference between class prediction and class comparison studies as well as the advantages and disadvantages of different modeling methods.

Humans↗
PubMed2006

ProRata: A quantitative proteomics program for accurate protein abundance ratio estimation with confidence interval evaluation.

A profile likelihood algorithm is proposed for quantitative shotgun proteomics to infer the abundance ratios of proteins from the abundance ratios of isotopically labeled peptides derived from proteolysis. Previously, we have shown that the estimation variability and bias of peptide abundance ratios can be predicted from their profile signal-to-noise ratios. Given multiple quantified peptides for a protein, the profile likelihood algorithm probabilistically weighs the peptide abundance ratios by their inferred estimation variability, accounts for their expected estimation bias, and suppresses contribution from outliers. This algorithm yields maximum likelihood point estimation and profile likelihood confidence interval estimation of protein abundance ratios. This point estimator is more accurate than an estimator based on the average of peptide abundance ratios. The confidence interval estimation provides an "error bar" for each protein abundance ratio that reflects its estimation precision and statistical uncertainty. The accuracy of the point estimation and the precision and confidence level of the interval estimation were benchmarked with standard mixtures of isotopically labeled proteomes. The profile likelihood algorithm was integrated into a quantitative proteomics program, called ProRata, freely available at www.MSProRata.org.

Algorithms↗
PubMed2026

Differential Proteomic Profiling of Responders and Non-responders to Direct-Acting Antivirals Treatment in Chronic Hepatitis C Virus Infection.

Hepatitis C Virus (HCV), particularly genotype 3 (GT-3), is highly prevalent in India and is associated with faster progression to cirrhosis, hepatocellular carcinoma, and higher treatment failure rates. Although Direct-Acting Antivirals (DAAs) have revolutionized HCV therapy, 5-10% of patients fail to achieve sustained virological response (SVR). This proteomic study aimed to identify changes in the proteomic profile before and after treatment of both responders and non-responders to HCV treatment. Paired plasma samples from HCV GT-3 infected patients were collected before and 12 weeks after initiating DAAs treatment, along with healthy controls. Quantitative proteomic analysis was performed on the paired samples. Differentially expressed proteins (DEPs) were identified and subjected to functional analysis including gene set enrichment analysis (GSEA) and protein-protein interaction (PPI) network analysis. GSEA revealed enrichment in extracellular matrix organization and innate immune pathways. Expression patterns of candidate proteins selected based on fold change and false discovery rate (FDR) criteria were further evaluated in an independent cohort. Western blot confirmed key expression trends of candidate proteins. Proteins linked to extracellular matrix remodeling and angiogenesis showed differential expression patterns. Successful validation of these candidate proteins in large independent cohorts holds potential to predict therapeutic outcomes.

Humans↗
PubMed2026

Serum Proteomic Profiling Reveals Renin-Associated Immune and Cytoskeletal Dysregulation in Post-COVID-19 Condition Patients with Secondary Adrenal Insufficiency.

Post-COVID-19 condition (PCC) with secondary adrenal insufficiency (SAI) involves multiorgan dysfunction, potentially linked to renin-angiotensin-aldosterone system dysregulation. The molecular basis of renin-associated pathology remains unclear. Here, PCC+SAI patients were stratified by upright renin into low- (<38.8&#x202f;pg/mL) and high-renin (&#x2265;38.8&#x202f;pg/mL) groups. Clinical, endocrine, and proteomic analyses were performed. We found that high-renin patients showed increased BMI, lipids, renin, and aldosterone, but reduced aldosterone-to-renin ratio. Proteomic annalysis identified 20 differentially expressed proteins (DEPs), including 17 upregulated and 3 downregulated proteins in Ren-H patients. Functional annotation revealed that 15 DEPs were immune-related (e.g., APOC4, APOE, C4BPA, CFAH, CFHR3, PF4V, PLF4), while FLNA and COF1 represented cytoskeletal proteins. These DEPs were primarily involved in immune response, complement and coagulation cascades, and MAPK signaling pathways. Correlation analyses indicated that upright renin was positively correlated with complement-related proteins and platelet-derived immune factors, while cytoskeletal proteins (FLNA, COF1) showed positive associations with serum Na+ levels. Additionally, white blood cell and platelet counts were positively correlated with the majority of DEPs. In conclusion, exploratory proteomic analyses suggest that elevated upright renin in PCC+SAI may be associated with immune dysregulation, complement activation, and cytoskeletal remodeling, offering novel insights into the endocrine-immune interactions driving postviral sequelae.

Humans↗
PubMed2026

Acidic Stress Induces Proteomic Reprogramming and Virulence-Associated Adaptation in Paracoccidioides brasiliensis.

Paracoccidioidomycosis (PCM) is a neglected systemic mycosis whose etiologic agents must adapt to acidic host niches such as phagolysosomes. Here, we used quantitative liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics to define the response of Paracoccidioides brasiliensis to acidic stress (pH 4.5) versus control pH (6.5) after 5 and 24 h. We identified and quantified 4374 proteins, including 327 and 722 differentially abundant proteins at 5 and 24 h, respectively, revealing time-dependent proteomic reprogramming. Enrichment analyses highlighted proteolysis, protein metabolism, organonitrogen metabolism, MAPK- and SNF1-like signaling, central carbon metabolism, tyrosine metabolism, and ergosterol biosynthesis as major acid-responsive processes. Complementary assays showed pH-dependent extracellular proteolytic activity, increased adhesion to A549 pulmonary epithelial cells, and dynamic ergosterol remodeling. The proteomic data further indicated increased abundance of moonlighting proteins linked to adhesion and metabolic enzymes associated with ATP generation and melanin precursor production. Together, these findings indicate that P. brasiliensis adapts to acidic environments through coordinated regulation of proteostasis, metabolism, signaling, host-cell interaction, and membrane homeostasis, supporting survival and virulence potential in acidic host microenvironments.

Paracoccidioides↗
PubMed2026

Enrichment Performance Assessment of Extracellular Vesicles Using Different Functionalized Magnetic Materials and Application in Urinary Proteomics of Prostate Cancer.

Extracellular vesicles (EVs) are lipid bilayer nanovesicles that mediate intercellular communication and hold significant potential for clinical applications. Although material-based isolation strategies offer promising alternatives to conventional methods, their relative performances have not been systematically evaluated. In this study, we conducted a comparative assessment of magnetic nanomaterials with distinct surface functionalities, including metal oxides (TiO2), metal-organic frameworks (UiO-66), biopolymeric materials (chitosan), and lipid probes (DSPE-PEG, DOPE-PEG, and CLS-PEG). A comprehensive evaluation across multiple dimensions including capture capacity, capture rate, sample volume, and product purity reveals that the bifunctional magnetic nanomaterial Fe3O4@UiO-66@DSPE material exhibits superior EV capture performance. This material enables the efficient and stable enrichment of high-purity EVs by synergizing Zr4+-phosphate coordination with lipid bilayer anchoring, and preserves EV biological integrity and activity. Meanwhile, this method could be highly compatible with proteomics, and over 1000 proteins are identified by proteomic analysis of urinary EVs, while 34 proteins are upregulated and 25 proteins are downregulated in prostate cancer patients relative to healthy donors. Notably, the differentially expressed proteins, such as AGT, ITIH4, and PGLYRP2, are associated with disease progression. Overall, this work highlights the superior performance of the Fe3O4@UiO-66@DSPE material for efficient and selective EV isolation. It provides a powerful tool for clinical liquid biopsy and proteomic biomarker discovery, enabling early diagnosis, prognostic evaluation, and precision therapy.

Humans↗
PubMedDate not supplied

Proteomics and bioinformatics strategies to design countermeasures against infectious threat agents.

The potential devastation resulting from an intentional outbreak caused by biological warfare agents such as Brucella abortus and Bacillus anthracis underscores the need for next generation vaccines. Proteomics, genomics, and systems biology approaches coupled with the bacterial ghost (BG) vaccine delivery strategy offer an ideal approach for developing safer, cost-effective, and efficacious vaccines for human use in a relatively rapid time frame. Critical to any subunit vaccine development strategy is the identification of a pathogen's proteins with the greatest potential of eliciting a protective immune response. These proteins are collectively referred to as the pathogen's immunome. Proteomics provides high-resolution identification of these immunogenic proteins using standard proteomic technologies, Western blots probed with antisera from infected patients, and the pathogen's sequenced and annotated genome. Selected immunoreactive proteins can be then cloned and expressed in nonpathogenic Gram-negative bacteria. Subsequently, a temperature shift or chemical induction process is initiated to induce expression of the PhiX174 E-lysis gene, whose protein product forms an E tunnel between the inner and outer membrane of the bacteria, expelling all intracellular contents. The BG vaccine system is a proven strategy developed for many different pathogens and tested in a complete array of animal models. The BG vaccine system also has great potential for producing multiagent vaccines for protection to multiple species in a single formulation.

Bacillus anthracis↗
PubMedDate not supplied

Comparison of statistical approaches for the analysis of proteome expression data of differentiating neural stem cells.

Comparative proteomic studies often use statistical tests included in the software for the analysis of digitized images of two-dimensional electrophoresis gels. As these programs include only limited capabilities for statistical analysis, many studies do not further describe their statistical approach. To find potential differences produced by different data processing, we compared the results of (1) Student's t-test using a spreadsheet program, (2) the intrinsic algorithms implemented in the Phoretix 2D gel analysis software, and (3) the SAM algorithm originally developed for microarray analysis. We applied the algorithms to proteome data of undifferentiated neural stem cells versus in vitro differentiated neural stem cells. We found (1) 367 spots differentially expressed using Student's t-test, (2) 203 spots using the algorithms in Phoretix 2D, and (3) 119 spots using the algorithms in SAM, respectively, with an overlap of 42 spots detected by all three algorithms. Applying different statistical approaches on the same dataset resulted in divergent set of protein spots labeled as statistically "significant". Currently, there is no agreement on statistical data processing of 2DE datasets, but the statistical tests applied in 2DE studies should be documented. Tools for the statistical analysis of proteome data should be implemented and documented in the existing 2DE software.

Animals↗
PubMedDate not supplied

Probability-based evaluation of peptide and protein identifications from tandem mass spectrometry and SEQUEST analysis: the human proteome.

Large-scale protein identifications from highly complex protein mixtures have recently been achieved using multidimensional liquid chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) and subsequent database searching with algorithms such as SEQUEST. Here, we describe a probability-based evaluation of false positive rates associated with peptide identifications from three different human proteome samples. Peptides from human plasma, human mammary epithelial cell (HMEC) lysate, and human hepatocyte (Huh)-7.5 cell lysate were separated by strong cation exchange (SCX) chromatography coupled offline with reversed-phase capillary LC-MS/MS analyses. The MS/MS spectra were first analyzed by SEQUEST, searching independently against both normal and sequence-reversed human protein databases, and the false positive rates of peptide identifications for the three proteome samples were then analyzed and compared. The observed false positive rates of peptide identifications for human plasma were significantly higher than those for the human cell lines when identical filtering criteria were used, suggesting that the false positive rates are significantly dependent on sample characteristics, particularly the number of proteins found within the detectable dynamic range. Two new sets of filtering criteria are proposed for human plasma and human cell lines, respectively, to provide an overall confidence of >95% for peptide identifications. The new criteria were compared, using a normalized elution time (NET) criterion (Petritis et al. Anal. Chem. 2003, 75, 1039-1048), with previously published criteria (Washburn et al. Nat. Biotechnol. 2001, 19, 242-247). The results demonstrate that the present criteria provide significantly higher levels of confidence for peptide identifications from mammalian proteomes without greatly decreasing the number of identifications.

Blood Proteins↗
PubMedDate not supplied

Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine.

The emergence of systems biology is bringing forth a new set of challenges for advancing science and technology. Defining ways of studying biological systems on a global level, integrating large and disparate data types, and dealing with the infrastructural changes necessary to carry out systems biology, are just a few of the extraordinary tasks of this growing discipline. Despite these challenges, the impact of systems biology will be far-reaching, and significant progress has already been made. Moving forward, the issue of how to use systems biology to improve the health of individuals must be a priority. It is becoming increasingly apparent that the field of systems biology and one of its important disciplines, proteomics, will have a major role in creating a predictive, preventative, and personalized approach to medicine. In this review, we define systems biology, discuss the current capabilities of proteomics and highlight some of the necessary milestones for moving systems biology and proteomics into mainstream health care.

Animals↗
PubMedDate not supplied

Exploring the context of the lung proteome within the airway mucosa following allergen challenge.

The lung proteome is a dynamic collection of specialized proteins related to pulmonary function. Many cells of different derivations, activation states, and levels of maturity contribute to the changing environment, which produces the lung proteome. Inflammatory cells reacting to environmental challenge, for example from allergens, produce and secrete proteins which have profound effects on both resident and nonresident cells located in airways, alveoli, and the vascular tree which provides blood cells to the parenchyma alveolar bed for gas exchange. In an experimental model of allergic airway inflammation, we have compared control and allergen challenged lung compartments to determine global protein expression patterns using 2D-gel electrophoresis and subsequent spot identification by MS/MS mass spectrometry. We have then specifically isolated the epithelial mucosal layer, which lines conducting airways, from control and allergen challenged lungs, using laser capture technology and performed proteome identification on these selected cell samples. A central component of our investigations has been to contextually relate the histological features of the dynamic pulmonary environment to the changes in protein expression observed following challenge. Our results provide new information of the complexity of the submucosa/epithelium interface and the mechanisms behind the transformation of airway epithelium from normal steady states to functionally activated states.

Allergens↗
PubMedDate not supplied

Utilizing human blood plasma for proteomic biomarker discovery.

Candidate proteomic biomarker discovery from human plasma holds both incredible clinical potential as well as significant challenges. The dynamic range of proteins within plasma is known to exceed 10(10), and many potential biomarkers are likely present at lower protein abundances. At present, proteomic based MS analyses provide a dynamic range typically not exceeding approximately 10(3) in a single spectrum, and approximately 10(4)-10(6) when combined with on-line separations (e.g., reversed-phase gradient liquid chromatography), and thus are generally insufficient for low level biomarker detection directly from human plasma. This limitation is providing an impetus for the development of experimental methodologies and strategies to increase the possible number of detections within this biofluid. Discussed is the diversity of available approaches currently used by our laboratory and others to utilize human plasma as a viable medium for biomarker discovery. Various separation, depletion, enrichment, and quantitative efforts as well as recent improvements in MS capabilities have resulted in measurable improvements in the detection and identification of lower abundance proteins (by approximately 10-10(2)). Despite these improvements, further advances are needed to provide a basis for discovery of candidate biomarkers at very low levels. Continued development of depletion and enrichment techniques, coupled with improved pre-MS separations (both at the protein and peptide level) holds promise in extending the dynamic range of proteomic analysis.

Biomarkers↗
PubMedDate not supplied

Data-dependent electron capture dissociation FT-ICR mass spectrometry for proteomic analyses.

Electron capture dissociation (ECD) offers many benefits for the analysis of peptides and proteins, and consequently shows great potential for the field of proteomics. Recent developments have reduced the time scale required for ECD to milliseconds resulting in the technique's compatibility with on-line separation techniques, e.g., HPLC. Here, we demonstrate incorporation of ECD into a high-throughput data-dependent LC-MS/MS approach for the analysis of proteomic samples. The approach is applied to analysis of the protein Fc-ROR2 isolated from chondrocytes and is the first example of LC-ECD-MS/MS of such a sample. Protein sequence coverage was 29%. Within that coverage, fifteen peptides were isolated and subjected to ECD. In most cases, the sequence tag generated by ECD was over 70% (in terms of the number of peptide backbone cleavages). The ECD data were searched against the nonredundant human NCBI database using the SEQUEST algorithm. Protein ROR2 was assigned, as was IgG (Fc domain). The results demonstrate the suitability of ECD as an integral technique in high-throughput proteomic strategies.

Algorithms↗
PubMedDate not supplied

Investigation of the mouse serum proteome.

With the rapid assimilation of genomic information and the equally impressive developments in the field of proteomics, there is an unprecedented interest in biomarker discovery. Although human biofluids represent increasingly attractive samples from which new and more accurate disease biomarkers may be found, the intrinsic person-to-person variability in these samples complicates their discovery. One of the most extensively used animal models for studying human disease is mouse because, unlike humans, they represent a highly controllable experimental model system. Unfortunately, very little is known about the proteomic composition of mouse serum. In this study, a multidimensional fractionation approach on both the protein and the peptide level that does not require depletion of highly abundant serum proteins was combined with tandem mass spectrometry to characterize proteins within mouse serum. Over 12 300 unique peptides that originate from 4567 unique proteins-approximately 16% of all known mouse proteins-were identified. The results presented here represent the broadest proteome coverage in mouse serum and provide a foundation from which quantitative comparisons can be made in this important animal model.

Amino Acid Sequence↗
PubMedDate not supplied

Identification and characterization of the Sulfolobus solfataricus P2 proteome.

Via combined separation approaches, a total of 1399 proteins were identified, representing 47% of the Sulfolobus solfataricus P2 theoretical proteome. This includes 1323 proteins from the soluble fraction, 44 from the insoluble fraction and 32 from the extra-cellular or secreted fraction. We used conventional 2-dimensional gel electrophoresis (2-DE) for the soluble fraction, and shotgun proteomics for all three cell fractions (soluble, insoluble, and secreted). Two gel-based fractionation methods were explored for shotgun proteomics, namely: (i) protein separation utilizing 1-dimensional gel electrophoresis (1-DE) followed by peptide fractionation by iso-electric focusing (IEF), and (ii) protein and peptide fractionation both employing IEF. Results indicate that a 1D-IEF fractionation workflow with three replicate mass spectrometric analyses gave the best overall result for soluble protein identification. A greater than 50% increment in protein identification was achieved with three injections using LC-ESI-MS/MS. Protein and peptide fractionation efficiency; together with the filtration criteria are also discussed.

Archaeal Proteins↗
PubMedDate not supplied

Development and evaluation of a micro- and nanoscale proteomic sample preparation method.

Challenges associated with the efficient and effective preparation of micro- and nanoscale (micro- and nanogram) clinical specimens for proteomic applications include the unmitigated sample losses that occur during the processing steps. Herein, we describe a simple "single-tube" preparation protocol appropriate for small proteomic samples using the organic cosolvent, trifluoroethanol (TFE) that circumvents the loss of sample by facilitating both protein extraction and protein denaturation without requiring a separate cleanup step. The performance of the TFE-based method was initially evaluated by comparisons to traditional detergent-based methods on relatively large scale sample processing using human breast cancer cells and mouse brain tissue. The results demonstrated that the TFE-based protocol provided comparable results to the traditional detergent-based protocols for larger, conventionally sized proteomic samples (>100 microg protein content), based on both sample recovery and numbers of peptide/protein identifications. The effectiveness of this protocol for micro- and nanoscale sample processing was then evaluated for the extraction of proteins/peptides and shown effective for small mouse brain tissue samples (approximately 30 microg total protein content) and also for samples of approximately 5000 MCF-7 human breast cancer cells (approximately 500 ng total protein content), where the detergent-based methods were ineffective due to losses during cleanup and transfer steps.

Animals↗