Serum Proteome Pool Changes in Type 2 Diabetic Patients Treated with Anakinra

Introduction

High glucose concentrations induce the production of IL-1β in human pancreatic beta cells leading to impaired insulin secretion, decreased cell proliferation and apoptosis. Blockade of IL-1 signalling with the recombinant human IL-1 receptor antagonist anakinra reduces HbA_1c in patients with type 2 diabetes. The aims of the present study were to identify: (1) candidate surrogates for improved glycemia in type 2 diabetic patients following treatment with anakinra, (2) proteins that change serum concentration because of anakinra treatment and (3) candidate biomarkers that may predict improved glycemia in type 2 diabetic subjects treated with anakinra.

Methods

Surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry was used to analyse serum from 67 type 2 diabetic patients who had received either placebo or anakinra for 13 weeks. Immunodepletion with magnetic protein G bead-coupled antibodies were used to identify three proteins and Western blotting confirmed the biomarker concentration pattern of four proteins.

Results

Twelve proteins, including transthyretin (TTR) and transferrin (Tf), were identified as candidate surrogates for improved glycemia. Six proteins, including retinol-binding protein 4 (RPB4) and a protein tentatively identified as modified apolipoprotein-A1 (apo-AI), increased expression as a consequence of anakinra treatment and four proteins were candidate biomarkers that may predict improved glycemia following anakinra treatment. Furthermore, we found increased RBP4 to be associated with improved beta cell secretory function and increased TTR, RBP4 and modified apo-AI (peak at 28,601 Da) to be associated with decreased inflammation.

Conclusions

Anakinra-induced changes in the serum proteome pool associated with a decreased cardiovascular disease risk, reduced inflammation and improved beta cell secretory function.     

Characterization of the Human Cervical Mucous Proteome

Introduction

Cervical cancer is among the most common cancers in women worldwide. Discovery of biomarkers for the early detection of cervical cancer would improve current screening practices and reduce the burden of disease.

Objective

In this study, we report characterization of the human cervical mucous proteome as the first step towards protein biomarker discovery.

Methods

The protein composition was characterized using one- and two-dimensional gel electrophoresis, and liquid chromatography coupled with mass spectrometry. We chose to use this combination of traditional biochemical techniques and proteomics to allow a more comprehensive analysis.

Results and Conclusion

A total of 107 unique proteins were identified, with plasma proteins being most abundant. These proteins represented the major functional categories of metabolism, immune response, and cellular transport. Removal of high molecular weight abundant proteins by immunoaffinity purification did not significantly increase the number of protein spots resolved. We also analyzed phosphorylated and glycosylated proteins by fluorescent post-staining procedures. The profiling of cervical mucous proteins and their post-translational modifications can be used to further our understanding of the cervical mucous proteome.     

ZIP8 expression in human proximal tubule cells, human urothelial cells transformed by Cd+2 and As+3 and in specimens of normal human urothelium and urothelial cancer

Background

ZIP8 functions endogenously as a Zn⁺²/HCO₃ ^- symporter that can also bring cadmium (Cd⁺²) into the cell. It has also been proposed that ZIP8 participates in Cd-induced testicular necrosis and renal disease. In this study real-time PCR, western analysis, immunostaining and fluorescent localization were used to define the expression of ZIP8 in human kidney, cultured human proximal tubule (HPT) cells, normal and malignant human urothelium and Cd⁺² and arsenite (As⁺³) transformed urothelial cells.

Results

It was shown that in the renal system both the non-glycosylated and glycosylated form of ZIP8 was expressed in the proximal tubule cells with localization of ZIP8 to the cytoplasm and cell membrane; findings in line with previous studies on ZIP8. The studies in the bladder were the first to show that ZIP8 was expressed in normal urothelium and that ZIP8 could be localized to the paranuclear region. Studies in the UROtsa cell line confirmed a paranuclear localization of ZIP8, however addition of growth medium to the cells increased the expression of the protein in the UROtsa cells. In archival human samples of the normal urothelium, the expression of ZIP8 was variable in intensity whereas in urothelial cancers ZIP8 was expressed in 13 of 14 samples, with one high grade invasive urothelial cancer showing no expression. The expression of ZIP8 was similar in the Cd⁺² and As⁺³ transformed UROtsa cell lines and their tumor transplants.

Conclusion

This is the first study which shows that ZIP8 is expressed in the normal urothelium and in bladder cancer. In addition the normal UROtsa cell line and its transformed counterparts show similar expression of ZIP8 compared to the normal urothelium and the urothelial cancers suggesting that the UROtsa cell line could serve as a model system to study the expression of ZIP8 in bladder disease.     

Discovery of pathway biomarkers from coupled proteomics and systems biology methods

Background

Breast cancer is worldwide the second most common type of cancer after lung cancer. Plasma proteome profiling may have a higher chance to identify protein changes between plasma samples such as normal and breast cancer tissues. Breast cancer cell lines have long been used by researches as model system for identifying protein biomarkers. A comparison of the set of proteins which change in plasma with previously published findings from proteomic analysis of human breast cancer cell lines may identify with a higher confidence a subset of candidate protein biomarker.

Results

In this study, we analyzed a liquid chromatography (LC) coupled tandem mass spectrometry (MS/MS) proteomics dataset from plasma samples of 40 healthy women and 40 women diagnosed with breast cancer. Using a two-sample t-statistics and permutation procedure, we identified 254 statistically significant, differentially expressed proteins, among which 208 are over-expressed and 46 are under-expressed in breast cancer plasma. We validated this result against previously published proteomic results of human breast cancer cell lines and signaling pathways to derive 25 candidate protein biomarkers in a panel. Using the pathway analysis, we observed that the 25 “activated” plasma proteins were present in several cancer pathways, including ‘Complement and coagulation cascades’, ‘Regulation of actin cytoskeleton’, and ‘Focal adhesion’, and match well with previously reported studies. Additional gene ontology analysis of the 25 proteins also showed that cellular metabolic process and response to external stimulus (especially proteolysis and acute inflammatory response) were enriched functional annotations of the proteins identified in the breast cancer plasma samples. By cross-validation using two additional proteomics studies, we obtained 86% and 83% similarities in pathway-protein matrix between the first study and the two testing studies, which is much better than the similarity we measured with proteins.

Conclusions

We presented a ‘systems biology’ method to identify, characterize, analyze and validate panel biomarkers in breast cancer proteomics data, which includes 1) t statistics and permutation process, 2) network, pathway and function annotation analysis, and 3) cross-validation of multiple studies. Our results showed that the systems biology approach is essential to the understanding molecular mechanisms of panel protein biomarkers.

     

Proteomic profiling of urine for the detection of colon cancer

Background

Colorectal cancer is the second most common cause of cancer related death in the developed world. To date, no blood or stool biomarkers with both high sensitivity and specificity for potentially curable early stage disease have been validated for clinical use. SELDI and MALDI profiling are being used increasingly to search for biomarkers in both blood and urine. Both techniques provide information predominantly on the low molecular weight proteome (<15 kDa). There have been several reports that colorectal cancer is associated with changes in the serum proteome that are detectable by SELDI and we hypothesised that proteomic changes would also be detectable in urine.

Results

We collected urine from 67 patients with colorectal cancer and 72 non-cancer control subjects, diluted to a constant protein concentration and generated MALDI and SELDI spectra. The intensities of 19 peaks differed significantly between cancer and non-cancer patients by both t-tests and after adjusting for confounders using multiple linear regressions. Logistic regression classifiers based on peak intensities identified colorectal cancer with up to 78% sensitivity at 87% specificity. We identified and independently quantified 3 of the discriminatory peaks using synthetic stable isotope peptides (an 1885 Da fragment of fibrinogen and hepcidin-20) or ELISA (β2-microglobulin).

Conclusion

Changes in the urine proteome may aid in the early detection of colorectal cancer.     

18O Labeling for a Quantitative Proteomic Analysis of Glycoproteins in Hepatocellular Carcinoma

Introduction

Quantitative proteomics using tandem mass spectrometry is an attractive approach for identification of potential cancer biomarkers. Fractionation of complex tissue samples into subproteomes prior to mass spectrometric analyses increases the likelihood of identifying cancer-specific proteins that might be present in low abundance. In this regard, glycosylated proteins are an interesting class of proteins that are already established as biomarkers for several cancers.

Materials and Methods

In this study, we carried out proteomic profiling of tumor and adjacent non-cancer liver tissues from hepatocellular carcinoma (HCC) patients. Glycoprotein enrichment from liver samples using lectin affinity chromatography and subsequent ¹⁸O/¹⁶O labeling of peptides allowed us to obtain relative abundance levels of lectin-bound proteins. As a complementary approach, we also examined the relative expression of proteins in HCC without glycoprotein enrichment. Lectin affinity enrichment was found to be advantageous to quantitate several interesting proteins, which were not detected in the whole proteome screening approach. We identified and quantitated over 200 proteins from the lectin-based approach. Interesting among these were fetuin, cysteine-rich protein 1, serpin peptidase inhibitor, leucine-rich alpha-2-glycoprotein 1, melanoma cell adhesion molecule, and heparan sulfate proteoglycan-2. Using lectin affinity followed by PNGase F digestion coupled to ¹⁸O labeling, we identified 34 glycosylation sites with consensus sequence N-X-T/S. Western blotting and immunohistochemical staining were carried out for several proteins to confirm mass spectrometry results.

Conclusion

This study indicates that quantitative proteomic profiling of tumor tissue versus non-cancerous tissue is a promising approach for the identification of potential biomarkers for HCC.     

Volatile metabolomic signature of bladder cancer cell lines based on gas chromatography–mass spectrometry

Introduction

Recent studies provide a convincing support that the presence of cancer cells in the body leads to the alteration of volatile organic compounds (VOCs) emanating from biological samples, particularly of those closely related with tumoral tissues. Thus, a great interest emerged for the study of cancer volatilome and subsequent attempts to confirm VOCs as potential diagnostic biomarkers.

Objectives

The aim of this study was to determine the volatile metabolomic signature of bladder cancer (BC) cell lines and provide an in vitro proof-of-principle that VOCs emanated into the extracellular medium may discriminate BC cells from normal bladder epithelial cells.

Methods

VOCs in the culture media of three BC cell lines (Scaber, J82, 5637) and one normal bladder cell line (SV-HUC-1) were extracted by headspace-solid phase microextraction and analysed by gas chromatography-mass spectrometry (HS-SPME/GC–MS). Two different pH (pH 2 and 7) were used for VOCs extraction to infer the best pH to be used in in vitro metabolomic studies.

Results

Multivariate analysis revealed a panel of volatile metabolites that discriminated cancerous from normal bladder cells, at both pHs, although a higher number of discriminative VOCs was obtained at neutral pH. Most of the altered metabolites were ketones and alkanes, which were generally increased in BC compared to normal cells, and alcohols, which were significantly decreased in BC cells. Among them, three metabolites, namely 2-pentadecanone, dodecanal and γ-dodecalactone (the latter only tentatively identified), stood out as particularly important metabolites and promising volatile biomarkers for BC detection. Furthermore, our results also showed the potential of VOCs in discriminating BC cell lines according to tumour grade and histological subtype.

Conclusions

We demonstrate that a GC–MS metabolomics-based approach for analysis of VOCs is a valuable strategy for identifying new and specific biomarkers that may improve BC diagnosis. Future studies should entail the validation of volatile signature found for BC cell lines in biofluids from BC patients.

     

Towards defining the nuclear proteome

Background

The nucleus is a complex cellular organelle and accurately defining its protein content is essential before any systematic characterization can be considered.

Results

We report direct evidence for 2,568 mammalian proteins within the nuclear proteome: the nuclear subcellular localization of 1,529 proteins based on a high-throughput subcellular localization protocol of full-length proteins and an additional 1,039 proteins for which clear experimental evidence is documented in published literature. This is direct evidence that the nuclear proteome consists of at least 14% of the entire proteome. This dataset was used to evaluate computational approaches designed to identify additional nuclear proteins.

Conclusion

This represents direct experimental evidence that the nuclear proteome consists of at least 14% of the entire proteome. This high-quality nuclear proteome dataset was used to evaluate computational approaches designed to identify additional nuclear proteins. Based on this analysis, researchers can determine the stringency and types of lines of evidence they consider to infer the size and complement of the nuclear proteome.     

Exploring metabolic pathway disruption in the subchronic phencyclidine model of schizophrenia with the Generalized Singular Value Decomposition

Background

One of the major goals in gene and protein expression profiling of cancer is to identify biomarkers and build classification models for prediction of disease prognosis or treatment response. Many traditional statistical methods, based on microarray gene expression data alone and individual genes' discriminatory power, often fail to identify biologically meaningful biomarkers thus resulting in poor prediction performance across data sets. Nonetheless, the variables in multivariable classifiers should synergistically interact to produce more effective classifiers than individual biomarkers.

Results

We developed an integrated approach, namely network-constrained support vector machine (netSVM), for cancer biomarker identification with an improved prediction performance. The netSVM approach is specifically designed for network biomarker identification by integrating gene expression data and protein-protein interaction data. We first evaluated the effectiveness of netSVM using simulation studies, demonstrating its improved performance over state-of-the-art network-based methods and gene-based methods for network biomarker identification. We then applied the netSVM approach to two breast cancer data sets to identify prognostic signatures for prediction of breast cancer metastasis. The experimental results show that: (1) network biomarkers identified by netSVM are highly enriched in biological pathways associated with cancer progression; (2) prediction performance is much improved when tested across different data sets. Specifically, many genes related to apoptosis, cell cycle, and cell proliferation, which are hallmark signatures of breast cancer metastasis, were identified by the netSVM approach. More importantly, several novel hub genes, biologically important with many interactions in PPI network but often showing little change in expression as compared with their downstream genes, were also identified as network biomarkers; the genes were enriched in signaling pathways such as TGF-beta signaling pathway, MAPK signaling pathway, and JAK-STAT signaling pathway. These signaling pathways may provide new insight to the underlying mechanism of breast cancer metastasis.

Conclusions

We have developed a network-based approach for cancer biomarker identification, netSVM, resulting in an improved prediction performance with network biomarkers. We have applied the netSVM approach to breast cancer gene expression data to predict metastasis in patients. Network biomarkers identified by netSVM reveal potential signaling pathways associated with breast cancer metastasis, and help improve the prediction performance across independent data sets.     

Identification of Tumor-Associated Proteins in Well Differentiated Laryngeal Squamous Cell Carcinoma by Proteomics

Introduction

This study established two-dimensional gel electrophoresis (2-DE) profiles for human well-differentiated laryngeal squamous cell carcinoma tissue and paired normal mucosa epithelia tissue and identified proteins with different expressions. Well-resolved and reproducible 2-DE patterns of well-differentiated laryngeal squamous cell carcinoma and adjacent normal mucosa were obtained.

Results

Thirteen proteins were preliminarily identified, among which ten proteins including cofilin-1, nuclear body protein SP140, GRP94, HSP 90, GSTP1-1, superoxide dismutase [Mn], cyclophilin A, proteasome activator complex subunit 2, apolipoprotein A-I precursor, and CaM-like protein were upregulated and three proteins including fatty acid-binding protein (E-FABP), calgranulin A, and calgranulin B were downregulated in laryngeal cancer tissue. The different expressions of cyclophilin A and MRP8 were confirmed by Western blotting.

Discussion

We first identified 13 proteins that might be associated with the tumorigenesis of the laryngeal squamous cell carcinoma. Some proteins were the products of oncogenes and apoptosis and others were related to signal transduction and immune defense. These extensive protein variations indicated that multiple protein molecules were simultaneously involved in the oncogenesis of laryngeal cancer, which in turn is a basis for the rational designs of diagnostic and therapeutic methods.     

Insulin receptor substrate-1 prevents autophagy-dependent cell death caused by oxidative stress in mouse NIH/3T3 cells

Background

Insulin receptor substrate (IRS)-1 is associated with tumorigenesis; its levels are elevated in several human cancers. IRS-1 protein binds to several oncogene proteins. Oxidative stress and reactive oxygen species (ROS) are involved in the initiation and progression of cancers. Cancer cells produce greater levels of ROS than normal cells do because of increased metabolic stresses. However, excessive production of ROS kills cancer cells. Autophagy usually serves as a survival mechanism in response to stress conditions, but excessive induction of autophagy results in cell death. In addition to inducing necrosis and apoptosis, ROS induces autophagic cell death. ROS inactivates IRS-1 mediated signaling and reduces intracellular IRS-1 concentrations. Thus, there is a complex relationship between IRS-1, ROS, autophagy, and cancer. It is not fully understood how cancer cells grow rapidly and survive in the presence of high ROS levels.

Methods and results

In this study, we established mouse NIH/3T3 cells that overexpressed IRS-1, so mimicking cancers with increased IRS-1 expression levels; we found that the IRS-1 overexpressing cells grow more rapidly than control cells do. Treatment of cells with glucose oxidase (GO) provided a continuous source of ROS; low dosages of GO promoted cell growth, while high doses induced cell death. Evidence for GO induced autophagy includes increased levels of isoform B-II microtubule-associated protein 1 light chain 3 (LC3), aggregation of green fluorescence protein-tagged LC3, and increased numbers of autophagic vacuoles in cells. Overexpression of IRS-1 resulted in inhibition of basal autophagy, and reduced oxidative stress-induced autophagy and cell death. ROS decreased the mammalian target of rapamycin (mTOR)/p70 ribosomal protein S6 kinase signaling, while overexpression of IRS-1 attenuated this inhibition. Knockdown of autophagy-related gene 5 inhibited basal autophagy and diminished oxidative stress-induced autophagy and cell death.

Conclusion

Our results suggest that overexpression of IRS-1 promotes cells growth, inhibits basal autophagy, reduces oxidative stress-induced autophagy, and diminishes oxidative stress-mediated autophagy-dependent cell death. ROS-mediated autophagy may occur via inhibition of IRS-1/phosphatidylinositol 3-kinase/mTOR signaling. Our data afford a plausible explanation for IRS-1 involvement in tumor initiation and progression.     

Quantitative proteomic analysis by iTRAQ<Superscript>®</Superscript> for the identification of candidate biomarkers in ovarian cancer serum

Background

Ovarian cancer is the most lethal gynecologic malignancy, with the majority of cases diagnosed at an advanced stage when treatments are less successful. Novel serum protein markers are needed to detect ovarian cancer in its earliest stage; when detected early, survival rates are over 90%. The identification of new serum biomarkers is hindered by the presence of a small number of highly abundant proteins that comprise approximately 95% of serum total protein. In this study, we used pooled serum depleted of the most highly abundant proteins to reduce the dynamic range of proteins, and thereby enhance the identification of serum biomarkers using the quantitative proteomic method iTRAQ^®.

Results

Medium and low abundance proteins from 6 serum pools of 10 patients each from women with serous ovarian carcinoma, and 6 non-cancer control pools were labeled with isobaric tags using iTRAQ^® to determine the relative abundance of serum proteins identified by MS. A total of 220 unique proteins were identified and fourteen proteins were elevated in ovarian cancer compared to control serum pools, including several novel candidate ovarian cancer biomarkers: extracellular matrix protein-1, leucine-rich alpha-2 glycoprotein-1, lipopolysaccharide binding protein-1, and proteoglycan-4. Western immunoblotting validated the relative increases in serum protein levels for several of the proteins identified.

Conclusions

This study provides the first analysis of immunodepleted serum in combination with iTRAQ^® to measure relative protein expression in ovarian cancer patients for the pursuit of serum biomarkers. Several candidate biomarkers were identified which warrant further development.

     

Plasma protein profiling of Mild Cognitive Impairment and Alzheimer’s disease using iTRAQ quantitative proteomics

Background

With the promise of disease modifying treatments, there is a need for more specific diagnosis and prognosis of Alzheimer’s disease (AD) and mild cognitive impairment (MCI). Plasma biomarkers are likely to be utilised to increase diagnostic accuracy and specificity of AD and cognitive decline.

Methods

Isobaric tags (iTRAQ) and proteomic methods were used to identify potential plasma biomarkers of MCI and AD. Relative protein expression level changes were quantified in plasma of 411 cognitively normal subjects, 19 AD patients and 261 MCI patients. Plasma was pooled into 4 groups including normal control, AD, amnestic single and multiple domain MCI (aMCI), and nonamnestic single and multiple domain MCI (nMCI). Western-blotting was used to validate iTRAQ data. Integrated function and protein interactions were explored using WEB based bioinformatics tools (DAVID v6.7 and STRING v9.0).

Results

In at least two iTRAQ replicate experiments, 30 proteins were significantly dysregulated in MCI and AD plasma, relative to controls. These proteins included ApoA1, ApoB100, complement C3, C4b-binding protein, afamin, vitamin D-binding protein precursor, isoform 1 of Gelsolin actin regulator, Ig mμ chain C region (IGHM), histidine-rich glycoprotein and fibrinogen β and γ chains. Western-blotting confirmed that afamin was decreased and IGHM was increased in MCI and AD groups. Bioinformatics results indicated that these dysregulated proteins represented a diversity of biological processes, including acute inflammatory response, cholesterol transport and blood coagulation.

Conclusion

These findings demonstrate that expression level changes in multiple proteins are observed in MCI and AD plasma. Some of these, such as afamin and IGHM, may be candidate biomarkers for AD and the predementia condition of MCI.     

Trypsin-induced proteome alteration during cell subculture in mammalian cells

Background

It is essential to subculture the cells once cultured cells reach confluence. For this, trypsin is frequently applied to dissociate adhesive cells from the substratum. However, due to the proteolytic activity of trypsin, cell surface proteins are often cleaved, which leads to dysregulation of the cell functions.

Methods

In this study, a triplicate 2D-DIGE strategy has been performed to monitor trypsin-induced proteome alterations. The differentially expressed spots were identified by MALDI-TOF MS and validated by immunoblotting.

Results

36 proteins are found to be differentially expressed in cells treated with trypsin, and proteins that are known to regulate cell metabolism, growth regulation, mitochondrial electron transportation and cell adhesion are down-regulated and proteins that regulate cell apoptosis are up-regulated after trypsin treatment. Further study shows that bcl-2 is down-regulated, p53 and p21 are both up-regulated after trypsinization.

Conclusions

In summary, this is the first report that uses the proteomic approach to thoroughly study trypsin-induced cell physiological changes and provides researchers in carrying out their experimental design.     

A proteome map of primary cultured rat Schwann cells

Background

Schwann cells (SCs) are the principal glial cells of the peripheral nervous system with a wide range of biological functions. SCs play a key role in peripheral nerve regeneration and are involved in several hereditary peripheral neuropathies. The objective of this study was to gain new insight into the whole protein composition of SCs.

Results

Two-dimensional liquid chromatography coupled with tandem mass spectrometry (2D LC-MS/MS) was performed to identify the protein expressions in primary cultured SCs of rats. We identified a total of 1,232 proteins, which were categorized into 20 functional classes. We also used quantitative real time RT-PCR and Western blot analysis to validate some of proteomics-identified proteins.

Conclusion

We showed for the first time the proteome map of SCs. Our data could serve as a reference library to provide basic information for understanding SC biology.     

MicroRNA expression signatures of bladder cancer revealed by deep sequencing

Background

MicroRNAs (miRNAs) are a class of small noncoding RNAs that regulate gene expression. They are aberrantly expressed in many types of cancers. In this study, we determined the genome-wide miRNA profiles in bladder urothelial carcinoma by deep sequencing.

Methodology/Principal Findings

We detected 656 differentially expressed known human miRNAs and miRNA antisense sequences (miRNA*s) in nine bladder urothelial carcinoma patients by deep sequencing. Many miRNAs and miRNA*s were significantly upregulated or downregulated in bladder urothelial carcinoma compared to matched histologically normal urothelium. hsa-miR-96 was the most significantly upregulated miRNA and hsa-miR-490-5p was the most significantly downregulated one. Upregulated miRNAs were more common than downregulated ones. The hsa-miR-183, hsa-miR-200b∼429, hsa-miR-200c∼141 and hsa-miR-17∼92 clusters were significantly upregulated. The hsa-miR-143∼145 cluster was significantly downregulated. hsa-miR-182, hsa-miR-183, hsa-miR-200a, hsa-miR-143 and hsa-miR-195 were evaluated by Real-Time qPCR in a total of fifty-one bladder urothelial carcinoma patients. They were aberrantly expressed in bladder urothelial carcinoma compared to matched histologically normal urothelium (p<0.001 for each miRNA).

Conclusions/Significance

To date, this is the first study to determine genome-wide miRNA expression patterns in human bladder urothelial carcinoma by deep sequencing. We found that a collection of miRNAs were aberrantly expressed in bladder urothelial carcinoma compared to matched histologically normal urothelium, suggesting that they might play roles as oncogenes or tumor suppressors in the development and/or progression of this cancer. Our data provide novel insights into cancer biology.