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PubMedDate not supplied

Comprehensive proteomic analysis of human pancreatic juice.

Proteomic technologies provide an excellent means for analysis of body fluids for cataloging protein constituents and identifying biomarkers for early detection of cancers. The biomarkers currently available for pancreatic cancer, such as CA19-9, lack adequate sensitivity and specificity contributing to late diagnosis of this deadly disease. In this study, we carried out a comprehensive characterization of the "pancreatic juice proteome" in patients with pancreatic adenocarcinoma. Pancreatic juice was first fractionated by 1-dimensional gel electrophoresis and subsequently analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS). A total of 170 unique proteins were identified including known pancreatic cancer tumor markers (e.g., CEA, MUC1) and proteins overexpressed in pancreatic cancers (e.g., hepatocarcinoma-intestine-pancreas/pancreatitis-associated protein (HIP/PAP) and lipocalin 2). In addition, we identified a number of proteins that have not been previously described in pancreatic juice (e.g., tumor rejection antigen (pg96) and azurocidin). Interestingly, a novel protein that is 85% identical to HIP/PAP was identified, which we have designated as PAP-2. The proteins identified in this study could be directly assessed for their potential as biomarkers for pancreatic cancer by quantitative proteomics methods or immunoassays.

Agglutinins↗

PubMed2025

Integrative glycomic analysis reveals the crucial role of protein glycosylation in fungal pathogenesis.

Protein glycosylation, a co- and post-translational modification that enhances the functional diversity of the proteome, contributes to various molecular and cellular functions by transferring different polysaccharides onto proteins. During the last decade, the role of glycosylation in plant pathogenic fungi has received significant attention, and glycoproteins are expected to play essential roles in various biological processes including pathogenicity. However, the comprehensive functional genetic analyses for protein glycosylation pathways and glycan structures of phytopathogenic fungi are still largely unknown. Here, we investigated the role of protein glycosylation in Fusarium graminearum by identifying 65 putative genes involved in protein glycosylation and characterizing their functions. Through cell wall component profiling and HPLC analysis, we characterized the overall N- and O-glycan structures in F. graminearum and found that deletion of ALG3 and ALG12 led to truncated core N-glycan structures. Quantitative proteomics analysis revealed that the truncated core N-glycans, generated by the loss of two key enzymes in the initial core N-glycosylation pathway, Alg3 and Alg12, affected a wide range of glycoproteins-including transcription factors, phosphatases, kinases, peroxidases, and other proteins involved in various biological processes-ultimately impacting the virulence of F. graminearum. This study elucidates the complex roles of glycosylation, highlighting the connections among genes involved in the protein glycosylation pathway, glycans, and glycoproteins in regulating the general biology and pathogenicity of F. graminearum. It also would be the fungal glycobiology study initiative.

Glycosylation↗

PubMed2005

Microglial activation induced by neurodegeneration: a proteomic analysis.

Neuroinflammation mediated by microglial activation appears to play an essential role in the pathogenesis of Parkinson disease; however, the mechanisms by which microglia are activated are not fully understood. Thus, we first evaluated the effects of two parkinsonian toxicants, manganese ethylene bisdithiocarbamate (Mn-EBDC) and 1-methyl-4-phenylpyridine (MPP+), on microglial activation as well as associated dopaminergic (DAergic) neurotoxicity in primary cell culture systems. The results demonstrated that, when rat primary mesencephalic neuron-enriched or neuron-microglia mixed cultures were treated with Mn-EBDC at 2-8 microm or MPP+ at 0.25-5 microm, respectively, for 7 days, both toxicants were capable of inducing DAergic neurodegeneration as well as activating microglia via a mechanism secondary to DAergic neurodegeneration. Furthermore activated microglia subsequently enhanced DAergic neurotoxicity induced by Mn-EBDC or MPP+. Detailed scrutiny of neuron-microglia interactions identified a fraction of the conditioned media derived from a DAergic cell line treated with Mn-EBDC or MPP+ that potently activated microglia. To further define potential mediators leading to microglial activation secondary to neurodegeneration, we utilized a quantitative proteomic technique termed SILAC (for stable isotope labeling by amino acids in cell culture) to compare the protein profiles of MPP+-treated cellular fraction that mediated microglial activation as compared with controls. The search revealed numerous novel proteins that are potentially important in neurodegeneration-mediated microglial activation, a process believed to be critical in Parkinson disease progression.

1-Methyl-4-phenylpyridinium↗

PubMed2024

Proteome Dynamics in iPSC-Derived Human Dopaminergic Neurons.

Dopaminergic neurons participate in fundamental physiological processes and are the cell type primarily affected in Parkinson's disease. Their analysis is challenging due to the intricate nature of their function, involvement in diverse neurological processes, and heterogeneity and localization in deep brain regions. Consequently, most of the research on the protein dynamics of dopaminergic neurons has been performed in animal cells ex vivo. Here we use iPSC-derived human mid-brain-specific dopaminergic neurons to study general features of their proteome biology and provide datasets for protein turnover and dynamics, including a human axonal translatome. We cover the proteome to a depth of 9409 proteins and use dynamic SILAC to measure the half-life of more than 4300 proteins. We report uniform turnover rates of conserved cytosolic protein complexes such as the proteasome and map the variable rates of turnover of the respiratory chain complexes in these cells. We use differential dynamic SILAC labeling in combination with microfluidic devices to analyze local protein synthesis and transport between axons and soma. We report 105 potentially novel axonal markers and detect translocation of 269 proteins between axons and the soma in the time frame of our analysis (120 h). Importantly, we provide evidence for local synthesis of 154 proteins in the axon and their retrograde transport to the soma, among them several proteins involved in RNA editing such as ADAR1 and the RNA helicase DHX30, involved in the assembly of mitochondrial ribosomes. Our study provides a workflow and resource for the future applications of quantitative proteomics in iPSC-derived human neurons.

Humans↗

PubMed2006

Quantitative analysis of complex peptide mixtures using FTMS and differential mass spectrometry.

Label-free LC-MS profiling is a powerful quantitative proteomic method to study relative peptide abundances between two or more biological samples. Here we demonstrate the use of a previously described comparative LC-MS method, differential mass spectrometry (dMS), to analyze high-resolution Fourier transform mass spectrometry (FTMS) data for detection and quantification of known peptide differences between two sets of complex mixtures. Six standard peptides were spiked into a processed plasma background at fixed ratios from 1.25:1 to 4:1 to make two sets of samples. The resulting mixtures were analyzed by microcapillary LC-FTMS and dMS. dMS successfully identified five out of the six peptides as statistically significant differences (p <or= 0.005). In this experiment, the smallest fold change reliably detected by our method was 1.5:1, and the errors of estimated ratios of concentrations were less than 20% for peptides spiked at 1.5:1 to 4:1. We conclude that LC-FTMS coupled with dMS is a useful label-free quantitative MS method that can be used to detect subtle yet statistically significant peptide differences in complex protein mixtures, including plasma samples.

Amino Acid Sequence↗

PubMed2006

Differentially isotope-coded N-terminal protein sulphonation: combining protein identification and quantification.

Most proteomic labelling technologies intend to improve protein quantification and/or facilitate (de novo) peptide sequencing. We present here a novel stable-isotope labelling method to simultaneously identify and quantify protein components in complex mixtures by specifically derivatizing the N-terminus of proteins with 4-sulphophenyl isothiocyanate (SPITC). Our approach combines protein identification with quantification through differential isotope-coded labelling at the protein N-terminus prior to digestion. The isotope spacing of 6 Da (unlabelled vs. six-fold 13C-labelled tag) between derivatized peptide pairs enables the detection on different MS platforms (MALDI and ESI). Optimisation of the reaction conditions using SPITC was performed on three model proteins. Improved detection of the N-terminally derivatized peptide compared to the native analogue was observed in negative-ion MALDI-MS. Simpler fragmentation patterns compared to native peptides facilitated protein identification. The 13C-labelled SPITC resulted in convenient peptide pair spacing without isotopic overlap and hence facilitated relative quantification by MALDI-TOF/TOF and LC-ESI-MS/MS. The combination of facilitated identification and quantification achieved by differentially isotope-coded N-terminal protein tagging with light/heavy SPITC represents, to our knowledge, a new approach to quantitative proteomics.

Animals↗

PubMed2005

Proteomic signatures for histological types of lung cancer.

We performed proteomic studies on lung cancer cells to elucidate the mechanisms that determine histological phenotype. Thirty lung cancer cell lines with three different histological backgrounds (squamous cell carcinoma, small cell lung carcinoma and adenocarcinoma) were subjected to two-dimensional difference gel electrophoresis (2-D DIGE) and grouped by multivariate analyses on the basis of their protein expression profiles. 2-D DIGE achieves more accurate quantification of protein expression by using highly sensitive fluorescence dyes to label the cysteine residues of proteins prior to two-dimensional polyacrylamide gel electrophoresis. We found that hierarchical clustering analysis and principal component analysis divided the cell lines according to their original histology. Spot ranking analysis using a support vector machine algorithm and unsupervised classification methods identified 32 protein spots essential for the classification. The proteins corresponding to the spots were identified by mass spectrometry. Next, lung cancer cells isolated from tumor tissue by laser microdissection were classified on the basis of the expression pattern of these 32 protein spots. Based on the expression profile of the 32 spots, the isolated cancer cells were categorized into three histological groups: the squamous cell carcinoma group, the adenocarcinoma group, and a group of carcinomas with other histological types. In conclusion, our results demonstrate the utility of quantitative proteomic analysis for molecular diagnosis and classification of lung cancer cells.

Adenocarcinoma↗

PubMed2006

Proteomic analysis of rat plasma by two-dimensional liquid chromatography and matrix-assisted laser desorption ionization time-of-flight mass spectrometry.

The proteomic analysis of plasma and serum samples represents a formidable challenge due to the presence of a few highly abundant proteins such as albumin and immunoglobulins. Detection of low abundance protein biomarkers therefore requires either the specific depletion of high abundance proteins using immunoaffinity columns and/or optimized protein fractionation methods based on charge, size or hydrophobicity. Here we describe a two-dimensional (2D) liquid chromatography separation method for the fractionation of rat plasma. In the first dimension proteins were separated by chromatofocusing according to their isoelectric point (pI). In the second dimension, proteins were further fractionated by non-porous, reversed-phase chromatography according to their hydrophobicity. The data from both separations was displayed as a 2D protein expression map of pI versus retention time (relative hydrophobicity). Both separations were carried out on the ProteomeLab PF 2D system (Beckman Coulter), an instrument platform that provides a high degree of automation and real-time monitoring of the separation process. The reproducibility of the first-dimension separation was evaluated in terms of pH gradient formation. The second-dimension separation was evaluated in terms of peak retention times on the reversed-phase column. We found in four consecutive chromatofocusing separations that the pH gradient differed by less than 0.2 pH units at any time during the elution step. Second dimension retention times of peaks from identical pI fractions differed by less than 7 s in six consecutive separations. Each 2D separation generated a total of 540 fractions which were analyzed by matrix assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS). We detected approximately 275 peptides and proteins with molecular masses ranging from 3 to 225 kDa. Most fractions were found to contain multiple low and high molecular weight proteins. Differential display of 2D protein expression maps from retinol-sufficient and -deficient rat plasma samples identified a fraction with several proteins that appeared to be down-regulated in the vitamin A-deficient animal. Quantitative proteomic analysis of complex samples such as plasma is still a difficult task. We discuss the potential of this approach for biomarker discovery and address the experimental challenges that remain.

Animals↗

PubMed2005

Software-induced variance in two-dimensional gel electrophoresis image analysis.

Experimental variability in 2-DE is well documented, but little attention has been paid to variability arising from postexperimental quantitative analyses using various 2-DE software packages. The performance of two 2-DE analysis software programs, Phoretix 2D Expression v2004 (Expression) and PDQuest 7.2 (PDQuest), was evaluated in this study. All available background subtraction and smoothing algorithms were tested using both data generated from one single 2-DE gel image, thus excluding experimental variance, and with authentic sets of replicate gels (n = 5). A slight shift of the image boundaries (the "cropping area") caused both programs to induce variance in protein spot quantification of otherwise identical gel images. The resulting variance for PDQuest (CV(mean) = 8%) was approximately twice that for Expression (CV(mean) = 4%). In authentic sets of replicate 2-DE gels (n = 5), the experimental variance confounded the software-induced variance to some extent. However, Expression still outperformed PDQuest, which exhibited software-induced variance as high as 25% of the total observed variance. Surprisingly, the complete omission of background subtraction algorithms resulted in the least amount of software-based variance. These data indicate that 2-DE gel analysis software constitutes a significant source of the variance observed in quantitative proteomics, and that the use of background subtraction algorithms can further increase the variance.

Algorithms↗

PubMedDate not supplied

Mechanistic data and cancer risk assessment: the need for quantitative molecular endpoints.

The cancer risk assessment process as currently proposed by the U.S. Environmental Protection Agency allows for the use of mechanistic data to inform the low-dose tumor response in humans and in laboratory animals. The aim is to reduce the reliance on defaults that introduce a relatively high level of uncertainty to the risk estimates. The types of data required for this purpose are those that help identify key events in tumor formation following exposure to environmental chemicals. Informative biomarkers of tumor responses could then be developed for describing the shape of a dose-response curve at low doses (i.e., a qualitative assessment) and for predicting tumor frequency at these low doses (i.e., a quantitative assessment). A number of recently developed molecular approaches could aid in the development of qualitatively and quantitatively informative biomarkers. An overview of these with examples of their use is presented. These methods include quantitative gene expression array techniques, quantitative proteomic assays, and the assessment of DNA alterations at the single gene level and at the genome level of detection. It is most likely that a combination of approaches at different levels of cellular organization (i.e., DNA, RNA, and protein) will be the most productive for biomarker development. The rapid progress that is being made will make this tool kit even more applicable for the cancer risk assessment process.

Biomarkers, Tumor↗

PubMed2026

Assessment the Effects of Roasted and Non-Roasted Coffee Extracts on Colon Cancer Cells.

Coffee extracts contain numerous bioactive compounds. Given the dietary link between coffee consumption and colorectal cancer, this study compared the effects of roasted and green (unroasted) coffee extracts on human colorectal cancer cells (HCT116) and non-cancerous fibroblasts (BJ-5ta) to evaluate how processing influences proliferation and molecular signaling. Real-time cell analysis (RTCA), qRT-PCR, and label-free quantitative proteomic analysis were performed to assess cellular responses. MTS and RTCA showed that in BJ-5Ta fibroblasts, coffee extracts increased proliferation in the order CNR < CAR < CAU < CNU, whereas the trend was reversed in HCT116 cancer cells. Proteomic analysis revealed that in BJ-5Ta cells, unroasted coffee extract caused downregulation of the ribosome pathway, and natural coffee extract caused downregulation of the gap junction pathway, indicating reduced protein synthesis and cell-cell communication as a potential stress-adaptive response. In contrast, in HCT116 cells, unroasted coffee extract upregulated the ribosome pathway. Also, natural coffee extract upregulated the pentose phosphate pathway in HCT116 cells, which may enhance NADPH production and reduce oxidative stress. Current evidence suggests coffee's bioactive compounds may have different effects varying by coffee extract type and their preparation.

Humans↗

PubMed2005

Numerical approaches for quantitative analysis of two-dimensional maps: a review of commercial software and home-made systems.

The present review attempts to cover a number of methods that have appeared in the last few years for performing quantitative proteome analysis. However, due to the large number of methods described for both electrophoretic and chromatographic approaches, we have limited this review to conventional two-dimensional (2-D) map analysis which couples orthogonally a charge-based step (isoelectric focusing) to a size-based separation step (sodium dodecyl sulfate-electrophoresis). The first and oldest method applied to 2-D map data reduction is based on statistical analysis performed on sets of gels via powerful software packages, such as Melanie, PDQuest, Z3 and Z4000, Phoretix and Progenesis. This method calls for separately running a number of replicas for control and treated samples. The two sets of data are then merged and compared via a number of software packages which we describe. In addition to commercially-available systems, a number of home made approaches for 2-D map comparison have been recently described and are also reviewed. They are based on fuzzyfication of the digitized 2-D gel image coupled to linear discriminant analysis, three-way principal component analysis or a combination of principal component analysis and soft-independent modeling of class analogy. These statistical tools appear to perform well in differential proteomic studies.

Cluster Analysis↗

PubMed2006

Selective detection of 2-nitrobenzenesulfenyl-labeled peptides by matrix-assisted laser desorption/ionization-time of flight mass spectrometry using a novel matrix.

The 2-nitrobenzenesulfenyl (NBS) method, which is useful for quantitative proteome analysis, is based on stable isotope labeling of tryptophan residues with NBS chloride ((12)C(6)-NBSCl or (13)C(6)-NBSCl). We found that 3-hydroxy-4-nitrobenzoic acid (3H4NBA) is a more suitable matrix than 2,5-dihydroxybenzoic acid (DHB) for detecting NBS-labeled peptides by MALDI-quadrupole IT (QIT)-TOF MS . Furthermore, NBS-labeled peptides were selectively ionized and detected in a mixture of NBS-labeled and unlabeled peptides. Labeled paired peaks were easily detected without enrichment, nonpaired labeled peaks were clearly distinguished from unlabeled contaminating peptides, and nitrotyrosine-containing peptides were also selectively detected on the 3H4NBA matrix, while by-product-peaks arising from nitrobenzene moieties were suppressed. The use of 3H4NBA as a comatrix with CHCA improved the sensitivity of detection while substantially retaining the selectivity of 3H4NBA. The 3H4NBA matrix offers great advantages in terms of simplicity, sensitivity, and usability when used for the NBS method and for MALDI-TOF MS analysis applied to compounds having a nitrobenzene ring.

Amino Acid Sequence↗

PubMed2006

Quantification of change in phosphorylation of BCR-ABL kinase and its substrates in response to Imatinib treatment in human chronic myelogenous leukemia cells.

Phosphorylation by the constitutively activated BCR-ABL tyrosine kinase is associated with the pathogenesis of the human chronic myelogenous leukemia (CML). It is difficult to characterize kinase response to stimuli or drug treatment because regulatory phosphorylation events are largely transient changes affecting low abundance proteins. Stable isotope labeling with amino acids in cell culture (SILAC) has emerged as a pivotal technology for quantitative proteomics. By metabolically labeling proteins with light or heavy tyrosine, we are able to quantify the change in phosphorylation of BCR-ABL kinase and its substrates in response to drug treatment in human CML cells. In this study, we observed that BCR-ABL kinase is phosphorylated at tyrosines 393 and 644, and that SH2-domain containing inositol phosphatase (SHIP)-2 and downstream of kinase (Dok)-2 are phosphorylated at tyrosine 1135 and 299, respectively. Based on the relative intensity of isotopic peptide pairs, we demonstrate that the level of phosphorylation of BCR-ABL kinase as well as SHIP-2 and Dok-2 is reduced approximately 90% upon treatment with Imatinib, a specific inhibitor of BCR-ABL kinase. Furthermore, proteins, such as SHIP-1, SH2-containing protein (SHC) and Casitas B-lineage lymphoma proto-oncogene (CBL), are also regulated by Imatinib. These results demonstrate the simplicity and utility of SILAC as a method to quantify dynamic changes in phosphorylation at specific sites in response to stimuli or drug treatment in cell culture.

Adaptor Proteins, Signal Transducing↗

PubMed2006

Improved 2-nitrobenzenesulfenyl method: optimization of the protocol and improved enrichment for labeled peptides.

We have developed the NBS (2-nitrobenzenesulfenyl) method, a quantitative proteome analysis method utilizing stable isotope labeling followed by mass spectrometry. The potential of this method was reported previously, and the procedure has now been further optimized. Here, we describe a procedure utilizing urea or guanidine hydrochloride as a protein denaturant, in conjunction with an improved chromatographic enrichment method for the NBS-labeled peptides using a phenyl resin column. By using this new protocol, both sample loss throughout the protocol and the elution of unwanted unlabeled peptides can be minimized, improving the efficiency of the analysis significantly.

Animals↗

PubMed2007

Direct identification of proteins from T47D cells and murine brain tissue by matrix-assisted laser desorption/ionization post-source decay/collision-induced dissociation.

The purpose of this study is to determine the feasibility of the direct matrix-assisted laser desorption/ionization (MALDI) identification of proteins in fixed T47D breast cancer cells and murine brain tissues. The ability to identify proteins from cells and tissue may lead to biomarkers that effectively predict the onset of defined disease states, and their dynamic behavior could be an important hint for drug target discoveries. Direct tissue application of trypsin allows protein identification in cells and tissues, while maintaining spatial integrity and intracellular organization. Using a chemical printer, matrix was co-registered on trypsinized human T47D breast cancer cells and cryo-preserved sections of murine brain tissue, followed by MALDI post-source decay (PSD) or MALDI collision-induced dissociation (CID), respectively. Mass-to-charge (m/z) data from the cells and brain tissues were processed using Mascot software interrogation of the National Center for Biotechnology Information (NCBI) database. Histone H2B was identified from cultured T47D human breast cancer cells. Tubulin beta2 was identified from mouse brain cortex following an induced stroke. These results suggest that MALDI PSD/CID, combined with bioinformatics, can be used for the direct identification of proteins from cells and tissues. Refinements in preparation techniques may improve this approach to provide a tool for quantitative proteomics and clinical analysis.

Animals↗

PubMed2025

Automated Machine Learning Tools to Build Regression Models for Schizosaccharomyces pombe Omics Data.

Machine learning is a powerful tool for analyzing biological data and making useful predictions. The surge of biological data from high-throughput omics technologies has raised the need for modeling approaches capable of tackling such amounts of data, which is pivotal to understanding the nature of complex molecular systems. Here, we show how to construct a simple model using automated machine learning (AutoML) to predict protein abundance in Schizosaccharomyces pombe, using data obtained from codon usage bias and quantitative proteomics.

Machine Learning↗

PubMed2026

APOM-associated inflammation and apoptosis in stroke-exacerbated myocardial infarction: implications for brain-heart interactions.

Brain-heart syndrome (BHS) describes cardiac dysfunction secondary to central nervous system injury, with acute ischemic stroke (AIS) serving as a critical driver that exacerbates myocardial infarction (MI). This study aimed to elucidate the role of Apolipoprotein M (APOM) in stroke-aggravated MI and to explore its underlying systemic and molecular mechanisms. Clinical data were analyzed to evaluate the correlation between stroke and MI. A combined mouse model of middle cerebral artery occlusion (MCAO) and MI was established to assess neurological and cardiac injury. Quantitative proteomics and Weighted Gene Co-expression Network Analysis (WGCNA) were employed to screen key differentially expressed proteins. The role of APOM in myocardial injury was validated using APOM-knockout (KO) mice. Furthermore, nuclear-cytoplasmic fractionation, immunofluorescence, and Western blot were performed to investigate its effects on the Saa1 and NF-κB signaling, NLRP3-related inflammatory signaling pathway, and lipid metabolism pathways. Clinical analysis indicated that stroke is a significant risk factor for MI (OR = 4.5). In the mouse model, MCAO significantly exacerbated post-MI electrocardiographic abnormalities, myocardial inflammatory response, while elevating circulating levels of cTnT and IL-1β. Proteomics identified a significant downregulation of APOM in the heart, brain, and serum post-stroke, a trend consistent with observations in AIS patients. Further experiments revealed that APOM deficiency markedly worsened cardiac conduction disturbances, histological damage, and inflammatory responses in MI mice. Mechanistically, the loss of APOM upregulates the acute-phase protein Saa1, triggers NF-κB phosphorylation and nuclear translocation, and enhances inflammatory signaling related to inflammasomes, while simultaneously mediating cytokine release from cardiomyocytes. Concurrently, APOM deficiency led to a significant decrease in sphingosine-1-phosphate (S1P) and also caused myocardial lipid droplet accumulation and metabolite changes. Additionally, the loss of APOM increased the expression of D-dimer and fibrinogen family proteins. Our findings suggest that APOM is a potential cardioprotective agent post-AIS. Downregulation of APOM may exacerbate myocardial injury after MI by elevating Saa1 expression, activating the NF-κB pathway and the inflammasome-mediated signaling, and inducing lipid metabolic disorders and coagulation-associated alterations. APOM may represent a potential therapeutic target for the intervention of brain-heart syndrome.

Animals↗