Calorimetry outside the box: a new window into the plasma proteome

Differential scanning calorimetry provides a new window into the plasma proteome. Plasma from normal individuals yields a characteristic, reproducible thermogram that appears to represent the weighted sum of denaturation profiles of the most abundant constituent plasma proteins. Plasma from diseased individuals yields dramatically different signature thermograms. Thermograms from individuals suffering from rheumatoid arthritis, systemic lupus, and Lyme disease were measured. Each disease appears to have a distinctive and characteristic thermogram. The difference in thermograms between normal and diseased individuals is not caused by radical changes in the concentrations of the most abundant plasma proteins but rather appears to result from interaction of as yet unknown biomarkers with the major plasma proteins. These results signal a novel use for calorimetry as a diagnostic tool.     

Plasma proteome analysis of cervical intraepithelial neoplasia and cervical squamous cell carcinoma

Although cervical cancer is preventable with early detection, it remains the second most common malignancy among women. An understanding of how proteins change in their expression during a particular diseased state such as cervical cancer will contribute to an understanding of how the disease develops and progresses. Potentially, it may also lead to the ability to predict the occurrence of the disease. With this in mind, we aimed to identify differentially expressed proteins in the plasma of cervical cancer patients. Plasma from control, cervical intraepithelial neoplasia (CIN) grade 3 and squamous cell carcinoma (SCC) stage IV subjects was resolved by two-dimensional gel electrophoresis and the resulting proteome profiles compared. Differentially expressed protein spots were then identified by mass spectrometry. Eighteen proteins were found to be differentially expressed in the plasma of CIN 3 and SCC stage IV samples when compared with that of controls. Competitive ELISA further validated the expression of cytokeratin 19 and tetranectin. Functional analyses of these differentially expressed proteins will provide further insight into their potential role(s) in cervical cancer-specific monitoring and therapeutics.     

Plasma-free amino acid profiling of cervical cancer and cervical intraepithelial neoplasia patients and its application for early detection

In this study, plasma-free amino acid profiles were used to investigate pre-cancerous cervical intraepithelial neoplasia (CIN) and cervical squamous cell carcinoma (CSCC) metabolic signatures in plasma. Additionally, the diagnostic potential of these profiles was assessed, as well as their ability to provide novel insight into CSCC metabolism and systemic effects. Plasma samples from CIN patients (n = 26), CSCC patients (n = 22), and a control healthy group (n = 35) were analyzed by high-performance liquid chromatography, and their spectral profiles were subjected to the t test for statistical significance. Potential metabolic biomarkers were identified using database comparisons that examine the significance of metabolites. Compared with healthy controls, patients with CIN and CSCC demonstrated lower levels of plasma amino acids; plasma levels of arginine and threonine were increased in CIN patients but were decreased in cervical cancer patients. Additionally, the levels of a larger group of amino acids (aspartate, glutamate, asparagine, serine, glycine, histidine, taurine, tyrosine, valine, methionine, lysine, isoleucine, leucine, and phenylalanine) were gradually reduced from CIN to invasive cancer. These findings suggest that plasma-free amino acid profiling has great potential for improving cancer screening and diagnosis and for understanding disease pathogenesis. Plasma-free amino acid profiles may have the potential be used to determine cancer diagnoses in the early stage from a single blood sample and may enhance our understanding of its mechanisms.     

Genome-wide methylation profiling identifies hypermethylated biomarkers in high-grade cervical intraepithelial neoplasia

Epigenetic modifications, such as aberrant DNA promoter methylation, are frequently observed in cervical cancer. Identification of hypermethylated regions allowing discrimination between normal cervical epithelium and high-grade cervical intraepithelial neoplasia (CIN2/3), or worse, may improve current cervical cancer population-based screening programs. In this study, the DNA methylome of high-grade CIN lesions was studied using genome-wide DNA methylation screening to identify potential biomarkers for early diagnosis of cervical neoplasia. Methylated DNA Immunoprecipitation (MeDIP) combined with DNA microarray was used to compare DNA methylation profiles of epithelial cells derived from high-grade CIN lesions with normal cervical epithelium. Hypermethylated differentially methylated regions (DMRs) were identified. Validation of nine selected DMRs using BSP and MSP in cervical tissue revealed methylation in 63.2–94.7% high-grade CIN and in 59.3–100% cervical carcinomas. QMSP for the two most significant high-grade CIN-specific methylation markers was conducted exploring test performance in a large series of cervical scrapings. Frequency and relative level of methylation were significantly different between normal and cancer samples. Clinical validation of both markers in cervical scrapings from patients with an abnormal cervical smear confirmed that frequency and relative level of methylation were related with increasing severity of the underlying CIN lesion and that ROC analysis was discriminative. These markers represent the COL25A1 and KATNAL2 and their observed increased methylation upon progression could intimate the regulatory role in carcinogenesis. In conclusion, our newly identified hypermethylated DMRs represent specific DNA methylation patterns in high-grade CIN lesions and are candidate biomarkers for early detection.     

Genome-wide methylation profiling identifies hypermethylated biomarkers in high-grade cervical intraepithelial neoplasia

Epigenetic modifications, such as aberrant DNA promoter methylation, are frequently observed in cervical cancer. Identification of hypermethylated regions allowing discrimination between normal cervical epithelium and high-grade cervical intraepithelial neoplasia (CIN2/3), or worse, may improve current cervical cancer population-based screening programs. In this study, the DNA methylome of high-grade CIN lesions was studied using genome-wide DNA methylation screening to identify potential biomarkers for early diagnosis of cervical neoplasia. Methylated DNA Immunoprecipitation (MeDIP) combined with DNA microarray was used to compare DNA methylation profiles of epithelial cells derived from high-grade CIN lesions with normal cervical epithelium. Hypermethylated differentially methylated regions (DMRs) were identified. Validation of nine selected DMRs using BSP and MSP in cervical tissue revealed methylation in 63.2–94.7% high-grade CIN and in 59.3–100% cervical carcinomas. QMSP for the two most significant high-grade CIN-specific methylation markers was conducted exploring test performance in a large series of cervical scrapings. Frequency and relative level of methylation were significantly different between normal and cancer samples. Clinical validation of both markers in cervical scrapings from patients with an abnormal cervical smear confirmed that frequency and relative level of methylation were related with increasing severity of the underlying CIN lesion and that ROC analysis was discriminative. These markers represent the COL25A1 and KATNAL2 and their observed increased methylation upon progression could intimate the regulatory role in carcinogenesis. In conclusion, our newly identified hypermethylated DMRs represent specific DNA methylation patterns in high-grade CIN lesions and are candidate biomarkers for early detection.     

Pretreatment plasma levels and diagnostic utility of hematopoietic cytokines in cervical cancer or cervical intraepithelial neoplasia patients

In this study, we compared plasma levels and the diagnostic utility of hematopoietic growth factors (HGFs) with SCC-Ag in cervical cancer patients in relation to control groups and cervical intraepithelial neoplasia (CIN) patients and healthy subjects. Pretreatment plasma levels of HGFs (SCF, GM-CSF, G-CSF and M-CSF) were determined by the use of immunoenzyme assay (ELISA), and SCC-Ag by chemiluminescent microparticle immunoassay (CMIA). Significantly different concentrations of GM-CSF, G-CSF and M-CSF were observed in the group of patients with cervical cancer and CIN compared to the healthy controls. Significant differences in plasma levels of GM-CSF and M-CSF between cervical cancer and benign lesions patients were also found. The HGFs and SCC-Ag diagnostic specificities received high values. The diagnostic sensitivity and the predictive value of a positive and negative test result were higher for M-CSF than for antigen SCC in the cancer group. The M-CSF area under the ROC curve (AUC) was the largest from hematopoietic cytokines and SCC-Ag. These results suggest the potential utility of M-CSF as a good candidate for a marker of cervical cancer as well as benign lesions of this organ (CIN).     

E4 Antibodies Facilitate Detection and Type-Assignment of Active HPV Infection in Cervical Disease

High-risk human papillomavirus (HPV) infections are the cause of nearly all cases of cervical cancer. Although the detection of HPV DNA has proved useful in cervical diagnosis, it does not necessarily predict disease presence or severity, and cannot conclusively identify the causative type when multiple HPVs are present. Such limitations may be addressed using complementary approaches such as cytology, laser capture microscopy, and/or the use of infection biomarkers. One such infection biomarker is the HPV E4 protein, which is expressed at high level in cells that are supporting (or have supported) viral genome amplification. Its distribution in lesions has suggested a role in disease staging. Here we have examined whether type-specific E4 antibodies may also allow the identification and/or confirmation of causal HPV-type. To do this, type-specific polyclonal and monoclonal antibodies against three E4 proteins (HPV-16, -18, and -58) were generated and validated by ELISA and western blotting, and by immunohistochemistry (IHC) staining of epithelial rafts containing these individual HPV types. Type-specific detection of HPV and its associated disease was subsequently examined using formalin-fixed paraffin-embedded cervical intra-epithelial neoplasias (CIN, (n = 247)) and normal controls (n = 28). All koilocytotic CIN1 lesions showed type-specific E4 expression of their respective HPV types. Differences were noted amongst E4 expression patterns in CIN3. HPV-18 E4 was not detected in any of the 6 HPV-18 DNA-positive CIN3 lesions examined, whereas in HPV-16 and -58 CIN3, 28/37 (76%) and 5/9 (55.6%) expressed E4 respectively, usually in regions of epithelial differentiation. Our results demonstrate that type-specific E4 antibodies can be used to help establish causality, as may be required when multiple HPV types are detected. The unique characteristics of the E4 biomarker suggest a role in diagnosis and patient management particularly when used in combination.     

An evaluation of p16INK4a expression in cervical intraepithelial neoplasia specimens, including women with HIV-1

Although several studies have evaluated the role of p16INK4a as a diagnostic marker of cervical intraepithelial neoplasia (CIN) and its association with disease progression, studies regarding the role of p16INK4a in human immunodeficiency virus (HIV)-infected patients remain scarce. The present study was designed to determine the potential utility of p16INK4a as a diagnostic marker for CIN and invasive cervical cancer in HIV-positive and negative cervical specimens. An immunohistochemical analysis of p16INK4a was performed in 326 cervical tissue microarray specimens. Performance indicators were calculated and compared using receiving operating characteristics curve (ROC)/area under the curve. In HIV-1-negative women, the percentage of cells that was positive for p16INK4a expression was significantly correlated with the severity of CIN (p < 0.0001). A ROC curve with a cut-off value of 55.28% resulted in a sensitivity of 89%, a specificity of 81%, a positive predictive value of 91% and a negative predictive value of 78%. HIV-seropositive women exhibited decreased expression of p16INK4a in CIN2-3 specimens compared with HIV-negative specimens (p = 0.031). The ROC data underscore the potential utility of p16INK4a under defined conditions as a diagnostic marker for CIN 2-3 staging and invasive cervical cancer. HIV-1 infection, however, is associated with relatively reduced p16INK4a expression in CIN 2-3.     

Differential scanning calorimetry as a complementary diagnostic tool for the evaluation of biological samples

BackgroundDifferential scanning calorimetry (DSC) is a tool for measuring the thermal stability profiles of complex molecular interactions in biological fluids. DSC profiles (thermograms) of biofluids provide specific signatures which are being utilized as a new diagnostic approach for characterizing disease but the development of these approaches is still in its infancy.MethodsThis article evaluates several approaches for the analysis of thermograms which could increase the utility of DSC for clinical application. Thermograms were analyzed using localized thermogram features and principal components (PCs). The performance of these methods was evaluated alongside six models for the classification of a data set comprised of 300 systemic lupus erythematosus (SLE) patients and 300 control subjects obtained from the Lupus Family Registry and Repository (LFRR).ResultsClassification performance was substantially higher using the penalized algorithms relative to localized features/PCs alone. The models were grouped into two sets, the first having smoother solution vectors but lower classification accuracies than the second with seemingly noisier solution vectors.ConclusionsCoupling thermogram technology with modern classification algorithms provides a powerful diagnostic approach for analysis of biological samples. The solution vectors from the models may reflect important information from the thermogram profiles for discriminating between clinical groups.General significanceDSC thermograms show sensitivity to changes in the bulk plasma proteome that correlate with clinical status. To move this technology towards clinical application the development of new approaches is needed to extract discriminatory parameters from DSC profiles for the comparison and diagnostic classification of patients. This article is part of a Special Issue entitled Microcalorimetry in the BioSciences — Principles and Applications, edited by Fadi Bou-Abdallah.     

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.     

Proteomic analysis of eccrine sweat: implications for the discovery of schizophrenia biomarker proteins

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) and multiple reaction monitoring mass spectrometry (MRM-MS) proteomics analyses were performed on eccrine sweat of healthy controls, and the results were compared with those from individuals diagnosed with schizophrenia (SZ). This is the first large scale study of the sweat proteome. First, we performed LC-MS/MS on pooled SZ samples and pooled control samples for global proteomics analysis. Results revealed a high abundance of diverse proteins and peptides in eccrine sweat. Most of the proteins identified from sweat samples were found to be different than the most abundant proteins from serum, which indicates that eccrine sweat is not simply a plasma transudate and may thereby be a source of unique disease-associated biomolecules. A second independent set of patient and control sweat samples were analyzed by LC-MS/MS and spectral counting to determine qualitative protein differential abundances between the control and disease groups. Differential abundances of selected proteins, initially determined by spectral counting, were verified by MRM-MS analyses. Seventeen proteins showed a differential abundance of approximately 2-fold or greater between the SZ pooled sample and the control pooled sample. This study demonstrates the utility of LC-MS/MS and MRM-MS as a viable strategy for the discovery and verification of potential sweat protein disease biomarkers.     

Plasma glycoprotein profiling for colorectal cancer biomarker identification by lectin glycoarray and lectin blot

Colorectal cancer (CRC) remains a major worldwide cause of cancer-related morbidity and mortality largely due to the insidious onset of the disease. The current clinical procedures utilized for disease diagnosis are invasive, unpleasant, and inconvenient; hence, the need for simple blood tests that could be used for the early detection of CRC. In this work, we have developed methods for glycoproteomics analysis to identify plasma markers with utility to assist in the detection of colorectal cancer (CRC). Following immunodepletion of the most abundant plasma proteins, the plasma N -linked glycoproteins were enriched using lectin affinity chromatography and subsequently further separated by nonporous silica reversed-phase (NPS-RP)-HPLC. Individual RP-HPLC fractions were printed on nitrocellulose coated slides which were then probed with lectins to determine glycan patterns in plasma samples from 9 normal, 5 adenoma, and 6 colorectal cancer patients. Statistical tools, including principal component analysis, hierarchical clustering, and Z-statistics analysis, were employed to identify distinctive glycosylation patterns. Patients diagnosed with colorectal cancer or adenomas were shown to have dramatically higher levels of sialylation and fucosylation as compared to normal controls. Plasma glycoproteins with aberrant glycosylation were identified by nano-LC-MS/MS, while a lectin blotting methodology was used to validate proteins with significantly altered glycosylation as a function of cancer progression. The potential markers identified in this study for diagnosis to distinguish colorectal cancer from adenoma and normal include elevated sialylation and fucosylation in complement C3, histidine-rich glycoprotein, and kininogen-1. These potential markers of colorectal cancer were subsequently validated by lectin blotting in an independent set of plasma samples obtained from 10 CRC patients, 10 patients with adenomas, and 10 normal subjects. These results demonstrate the utility of this strategy for the identification of N -linked glycan patterns as potential markers of CRC in human plasma, and may have the utility to distinguish different disease states.     

Optimizing Size Exclusion Chromatography for Extracellular Vesicle Enrichment and Proteomic Analysis from Clinically Relevant Samples

The field of extracellular vesicle (EV) research has rapidly expanded in recent years, with particular interest in their potential as circulating biomarkers. Proteomic analysis of EVs from clinical samples is complicated by the low abundance of EV proteins relative to highly abundant circulating proteins such as albumin and apolipoproteins. To overcome this, size exclusion chromatography (SEC) has been proposed as a method to enrich EVs whilst depleting protein contaminants; however, the optimal SEC parameters for EV proteomics have not been thoroughly investigated. Here, quantitative evaluation and optimization of SEC are reported for separating EVs from contaminating proteins. Using a synthetic model system followed by cell line‐derived EVs, it is found that a 10 mL Sepharose 4B column in PBS produces optimal resolution of EVs from background protein. By spiking‐in cancer cell‐derived EVs to healthy plasma, it is shown that some cancer EV‐associated proteins are detectable by nano‐LC‐MS/MS when as little as 1% of the total plasma EV number are derived from a cancer cell line. These results suggest that an optimized SEC and nanoLC‐MS/MS workflow may be sufficiently sensitive for disease EV protein biomarker discovery from patient‐derived clinical samples.     

Affinity prefractionation for MS‐based plasma proteomics

The plasma proteome has proven to be one of the most challenging proteomes to profile using currently available proteomics technologies. A plethora of methodologies have been used to profile human plasma in order to discover potential biomarkers for disease and for therapy optimization. Affinity‐based prefractionation coupled to MS has been shown to be one of the most successful ways to dig deeper into the plasma proteome. Depletion of high abundant plasma proteins is becoming an initial method of choice in any plasma profiling project. However, several other affinity‐based enrichment methods have been published in recent years. Here we review both protein and peptide affinity prefractionation methods coupled with MS‐based proteomics. Analysis of the proportion of cellular and extracellular annotated proteins of publicly available MS plasma proteomics data is performed to estimate the analytical depth of various prefractionation methods.     

Higher dimensional (Hi-D) separation strategies dramatically improve the potential for cancer biomarker detection in serum and plasma

The plasma proteome has a wide dynamic range of protein concentrations and is dominated by a few highly abundant proteins. Discovery of novel cancer biomarkers using proteomics is particularly challenging because specific biomarkers are expected to be low abundance proteins with normal blood concentrations of low nanograms per milliliter or less. Conventional, one- and two-dimensional proteomic methods including 2D PAGE, 2D DIGE, LC-MS/MS, and LC/LC-MS/MS do not have the capacity to consistently detect many proteins in this range. In contrast, new higher dimensional (Hi-D) separation strategies, utilizing more than two dimensions of fractionation, can profile the low abundance proteome.     

KYSS: Mass spectrometry data quality assessment for protein analysis and large-scale proteomics

We introduce the computer tool “Know Your Samples” (KYSS) for assessment and visualisation of large scale proteomics datasets, obtained by mass spectrometry (MS) experiments. KYSS facilitates the evaluation of sample preparation protocols, LC peptide separation, and MS and MS/MS performance by monitoring the number of missed cleavages, precursor ion charge states, number of protein identifications and peptide mass error in experiments. KYSS generates several different protein profiles based on protein abundances, and allows for comparative analysis of multiple experiments. KYSS was adapted for blood plasma proteomics and provides concentrations of identified plasma proteins. We demonstrate the utility of the KYSS tool for MS based proteome analysis of blood plasma and for assessment of hydrogel particles for depletion of abundant proteins in plasma. The KYSS software is open source and is freely available at http://kyssproject.github.io/.     

Identification of protein fragments as pattern features in MALDI-MS analyses of serum

The use of matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) to acquire spectral profiles has become a common approach to detect proteomic biomarkers of disease. MALDI-MS signals may represent both intact proteins as well as proteolysis products. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis can tentatively identify the corresponding proteins Here, we describe the application of a data analysis utility called FragMint, which combines MALDI-MS spectral data with LC-MS/MS based protein identifications to generate candidate protein fragments consistent with both types of data. This approach was used to identify protein fragments corresponding to spectral signals in MALDI-MS analyses of unfractionated human serum. The serum also was analyzed by one-dimensional SDS-PAGE and bands corresponding to the MALDI-MS signal masses were excised and subjected to in-gel digestion and LC-MS/MS analysis. Database searches mapped all of the identified peptides to abundant blood proteins larger than the observed MALDI-MS signals. FragMint identified fragments of these proteins that contained the MS/MS identified sequences and were consistent with the observed MALDI-MS signals. This approach should be generally applicable to identify protein species corresponding to MALDI-MS signals.     

iTRAQ-based proteomic profiling of breast cancer cell response to doxorubicin and TRAIL

Breast cancer is a molecularly heterogeneous disease, and predicting response to chemotherapy remains a major clinical challenge. To minimize adverse side-effects or cumulative toxicity in patients unlikely to benefit from treatment, biomarkers indicating treatment efficacy are critically needed. iTRAQ labeling coupled with multidimensional LC-MS/MS of the enriched mitochondria and endoplasmic reticulum fraction, key organelles regulating apoptosis, has led to the discovery of several differentially abundant proteins in breast cancer cells treated with the chemotherapeutic agent doxorubicin followed by the death receptor ligand, TRAIL, among 571 and 801 unique proteins identified in ZR-75-1 and MDA-MB-231 breast cancer cell lines, respectively. The differentially abundant proteins represent diverse biological processes associated with cellular assembly and organization, molecular transport, oxidative stress, cell motility, cell death, and cancer. Despite many differences in molecular phenotype between the two breast cancer cell lines, a comparison of their subproteomes following drug treatment revealed three proteins displaying common regulation: PPIB, AHNAK, and SLC1A5. Changes in these proteins, detected by iTRAQ, were confirmed by immunofluorescence, visualized by confocal microscopy. These novel potential biomarkers may have clinical utility for assessing response to cancer treatment and may provide insight into new therapeutic targets for breast cancer.     

A novel approach and protocol for discovering extremely low-abundance proteins in serum

The proteomic analysis of serum (plasma) has been a major approach to determining biomarkers essential for early disease diagnoses and drug discoveries. The determination of these biomarkers, however, is analytically challenging since the dynamic concentration range of serum proteins/peptides is extremely wide (more than 10 orders of magnitude). Thus, the reduction in sample complexity prior to proteomic analyses is essential, particularly in analyzing low-abundance protein biomarkers. Here, we demonstrate a novel approach to the proteomic analyses of human serum that uses an originally developed serum protein separation device and a sequentially linked 3-D-LC-MS/MS system. Our hollow-fiber-membrane-based serum pretreatment device can efficiently deplete high-molecular weight proteins and concentrate low-molecular weight proteins/peptides automatically within 1 h. Four independent analyses of healthy human sera pretreated using this unique device, followed by the 3-D-LC-MS/MS successfully produced 12 000-13 000 MS/MS spectra and hit around 1800 proteins (>95% reliability) and 2300 proteins (>80% reliability). We believe that the unique serum pretreatment device and proteomic analysis protocol reported here could be a powerful tool for searching physiological biomarkers by its high throughput (3.7 days per one sample analysis) and high performance of finding low abundant proteins from serum or plasma samples.