Automated serum peptide profiling

Peptide-Peptide Co-Assembly: A Design Strategy for Functional Detection of C-peptide, A Biomarker of Diabetic Neuropathy

Diabetes-related neuropathy is a debilitating condition that may be averted if it can be detected early. One possible way this can be achieved at low cost is to utilise peptides to detect C-peptide, a biomarker of diabetic neuropathy. This depends on peptide-peptide co-assembly, which is currently in a nascent stage of intense study. Instead, we propose a bead-based triple-overlay combinatorial strategy that can preserve inter-residue information during the screening process for a suitable complementary peptide to co-assemble with C-peptide. The screening process commenced with a pentapeptide general library, which revealed histidine to be an essential residue. Further screening with seven tetrapeptide focused libraries led to a table of self-consistent peptide sequences that included tryptophan and lysine at high frequencies. Three complementary nonapeptides (9mer com-peptides), wpkkhfwgq (Trp-D), kwkkhfwgq (Lys-D), and KWKKHFWGQ (Lys-L) (as a negative control) were picked from this table for co-assembly studies with C-peptide. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) and circular dichroism (CD) spectroscopies were utilized to study inter-peptide interactions and changes in secondary structures respectively. ATR-FTIR studies showed that there is indeed inter-peptide interaction between C-peptide and the tryptophan residues of the 9mer com-peptides. CD studies of unaggregated and colloidal C-peptide with the 9mer com-peptides suggest that the extent of co-assembly of C-peptide with Trp-D is greatest, followed by Lys-D and Lys-L. These results are promising and indicate that the presented strategy is viable for designing and evaluating longer complementary peptides, as well as complementary peptides for co-assembly with other polypeptides of interest and importance. We discuss the possibility of designing complementary peptides to inhibit toxic amyloidosis with this approach.

Role of cofilin‑1 in arsenic trioxide‑induced apoptosis of NB4‑R1 cells

All-trans retinoic acid (ATRA) and arsenic trioxide (As₂O₃) are currently first-line treatments for acute promyelocytic leukemia (APL). However, a number of patients with APL are resistant to ATRA but still sensitive to As₂O₃, and the underlying mechanisms of this remain unclear. In the present study, two-dimensional gel electrophoresis, mass spectrometry and other proteomic methods were applied to screen and identify the differentially expressed proteins between the retinoic acid-sensitive cell lines and drug-resistant cell lines. The results demonstrated that in retinoic acid-resistant NB4-R1 cells, the protein expression of cofilin-1 was markedly increased compared with that in the drug-sensitive NB4 cells. Subsequently, the effects of cofilin-1 on As₂O₃-induced apoptosis in NB4-R1 cells were further investigated. The results revealed that cell viability was markedly suppressed and apoptosis was increased in the As₂O₃-treated NB4-R1 cells, with increased expression levels of cleaved-poly (ADP-ribose) polymerase and cleaved-caspase 12. Cofilin-1 expression was significantly decreased at both the mRNA and protein levels in the As₂O₃-treated group compared with the control. Western blotting further revealed that As₂O₃ treatment decreased the cytoplasmic cofilin-1 level but increased its expression in the mitochondrion. However, the opposite effects of As₂O₃ on the cytochrome C distribution were found in NB4-R1 cells. This suggested that As₂O₃ can induce the transfer of cofilin-1 from the cytoplasm to mitochondria and trigger the release of mitochondrial cytochrome C in NB4-R1 cells. Moreover, cofilin-1 knockdown by its specific short hairpin RNA significantly suppressed As₂O₃-induced NB4-R1 cell apoptosis and inhibited the release of mitochondrial cytochrome C. Whereas, overexpression of cofilin-1 using a plasmid vector carrying cofilin-1 increased the release of cytochrome C into the cytoplasm from the mitochondria in As₂O₃-treated NB4-R1 cells. In conclusion, cofilin-1 played a role in As₂O₃-induced NB4-R1 cell apoptosis and it might be a novel target for APL treatment.

Quantitative Proteome Heterogeneity in Myeloproliferative Neoplasm Subtypes and Association with JAK2 Mutation Status

Apart from well-known genetic abnormalities, several studies have reported variations in protein expression in Philadelphia-negative myeloproliferative neoplasm (MPN) patients that could contribute toward their clinical phenotype. In this context, a quantitative mass spectrometry proteomics protocol was used to identify differences in the granulocyte proteome with the goal to characterize the pathogenic role of aberrant protein expression in MPNs. LC/MS-MS (LTQ Orbitrap) coupled to iTRAQ labeling showed significant and quantitative differences in protein content among various MPN subtypes [polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF)], and according to the genetic status of JAK2 (JAK2V617F presence and JAK2V617F allele burden). A number of differentially expressed proteins were identified, with the most frequent being members of the RAS GTPase family and oxidative stress regulatory proteins. Subsequent analysis found that calreticulin (CALR), known to be involved in calcium homeostasis and apoptotic signaling, was overexpressed in JAK2V617F granulocytes compared with JAK2 wild type and independently of the JAK2V617F allele burden. Finally, it was demonstrated, in a Ba/F3 cell model, that increased calreticulin expression was directly linked to JAK2V617F and could be regulated by JAK2 kinase inhibitors.Implications: In conclusion, these results reveal proteome alterations in MPN granulocytes depending on the phenotype and genotype of patients, highlighting new oncogenic mechanisms associated with JAK2 mutations and overexpression of calreticulin. Mol Cancer Res; 15(7); 852-61. ©2017 AACR.

Ph(-) myeloproliferative neoplasm red blood cells display deregulation of IQGAP1-Rho GTPase signaling depending on CALR/JAK2 status

Besides genetic abnormalities in MPN patients, several studies have reported alterations in protein expression that could contribute towards the clinical phenotype. However, little is known about protein modifications in Ph^- MPN erythrocytes. In this context, we used a quantitative mass spectrometry proteomics approach to study the MPN erythrocyte proteome. LC-MS/MS (LTQ Orbitrap) analysis led to the identification of 51 and 86 overexpressed proteins in Polycythemia Vera and Essential Thrombocythemia respectively, compared with controls. Functional comparison using pathway analysis software showed that the Rho GTPase family signaling pathways were deregulated in MPN patients. In particular, IQGAP1 was significantly overexpressed in MPNs compared with controls. Additionally, Western-blot analysis not only confirmed IQGAP1 overexpression, but also showed that IQGAP1 levels depended on the patient's genotype. Moreover, we found that in JAK2V617F patients IQGAP1 could bind RhoA, Rac1 and Cdc42 and consequently recruit activated GTP-Rac1 and the cytoskeleton motility protein PAK1. In CALR(+) patients, IQGAP1 was not overexpressed but immunoprecipitated with RhoGDI. In JAK2V617F transduced Ba/F3 cells we confirmed JAK2 inhibitor-sensitive overexpression of IQGAP1/PAK1. Altogether, our data demonstrated alterations of IQGAP1/Rho GTPase signaling in MPN erythrocytes dependent on JAK2/CALR status, reinforcing the hypothesis that modifications in erythrocyte signaling pathways participate in Ph^- MPN pathogenesis.

Identification of Serum Peptidome Signatures of Non-Small Cell Lung Cancer

Due to high mortality rates of lung cancer, there is a need for identification of new, clinically useful markers, which improve detection of this tumor in early stage of disease. In the current study, serum peptide profiling was evaluated as a diagnostic tool for non-small cell lung cancer patients. The combination of the ZipTip technology with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) for the analysis of peptide pattern of cancer patients (n = 153) and control subjects (n = 63) was presented for the first time. Based on the observed significant differences between cancer patients and control subjects, the classification model was created, which allowed for accurate group discrimination. The model turned out to be robust enough to discriminate a new validation set of samples with satisfactory sensitivity and specificity. Two peptides from the diagnostic pattern for non-small cell lung cancer (NSCLC) were identified as fragments of C3 and fibrinogen α chain. Since ELISA test did not confirm significant differences in the expression of complement component C3, further study will involve a quantitative approach to prove clinical utility of the other proteins from the proposed multi-peptide cancer signature.

Influence of honeybee sting on peptidome profile in human serum

The aim of this study was to explore the serum peptide profiles from honeybee stung and non-stung individuals. Two groups of serum samples obtained from 27 beekeepers were included in our study. The first group of samples was collected within 3 h after a bee sting (stung beekeepers), and the samples were collected from the same person a second time after at least six weeks after the last bee sting (non-stung beekeepers). Peptide profile spectra were determined using MALDI-TOF mass spectrometry combined with Omix, ZipTips and magnetic beads based on weak-cation exchange (MB-WCX) enrichment strategies in the mass range of 1–10 kDa. The samples were classified, and discriminative models were established by using the quick classifier, genetic algorithm and supervised neural network algorithms. All of the statistical algorithms used in this study allow distinguishing analyzed groups with high statistical significance, which confirms the influence of honeybee sting on the serum peptidome profile. The results of this study may broaden the understanding of the human organism’s response to honeybee venom. Due to the fact that our pilot study was carried out on relatively small datasets, it is necessary to conduct further proteomic research of the response to honeybee sting on a larger group of samples.

Accessing to the minor proteome of red blood cells through the influence of the nanoparticle surface properties on the corona composition

Nanoparticle (NP)-protein interactions in complex samples have not yet been clearly understood. Nevertheless, several studies demonstrated that NP's physicochemical features significantly impact on the protein corona composition. Taking advantage of the NP potential to harvest different subsets of proteins, we assessed for the first time the capacity of three kinds of superparamagnetic NPs to highlight the erythrocyte minor proteome. Using both qualitative and quantitative proteomics approaches, nano-liquid chromatography-tandem mass spectrometry allowed the identification of 893 different proteins, confirming the reproducible capacity of NPs to increase the number of identified proteins, through a reduction of the sample concentration range and the capture of specific proteins on the three different surfaces. These NP-specific protein signatures revealed significant differences in their isoelectric point and molecular weight. Moreover, this NP strategy offered a deeper access to the erythrocyte proteome highlighting several signaling pathways implicated in important erythrocyte functions. The automated potentiality, the reproducibility, and the low-consuming sample demonstrate the strong compatibility of our strategy for large-scale clinical studies and may become a standardized sample preparation in future erythrocyte-associated proteomics studies.

Discovery of serum biomarkers of ovarian cancer using complementary proteomic profiling strategies

Purpose

Ovarian cancer is a devastating disease and biomarkers for its early diagnosis are urgently required. Serum may be a valuable source of biomarkers that may be revealed by proteomic profiling. Herein, complementary serum protein profiling strategies were employed for discovery of biomarkers that could discriminate cases of malignant and benign ovarian cancer.

Experimental design

Identically collected and processed serum samples from 22 cases of invasive epithelial ovarian cancer, 45 benign ovarian neoplasms, and 64 healthy volunteers were subjected to immunodepletion and protein equalization coupled to 2D‐DIGE/MS and multidimensional fractionation coupled to SELDI‐TOF profiling with MS/MS for protein identification. Selected candidates were verified by ELISA in samples from malignant (n = 70) and benign (n = 89) cases and combined marker panels tested against serum CA125.

Results

Both profiling platforms were complementary in identifying biomarker candidates, four of which (A1AT, SLPI, APOA4, VDBP) significantly discriminated malignant from benign cases. However, no combination of markers was as good as CA125 for diagnostic accuracy. SLPI was further tested as an early marker using prediagnosis serum samples. While it rose in cases toward diagnosis, it did not discriminate prediagnosis cases from controls.

Conclusions and clinical relevance

The candidate biomarkers warrant further validation in independent sample sets.

Inhibition of circulating dipeptidyl peptidase 4 activity in patients with metastatic prostate cancer

Cancer is responsible for many deaths and is a major source of healthcare expenditures. The identification of new, non-invasive biomarkers might allow improvement of the direct diagnostic or prognostic ability of already available tools. Here, we took the innovative approach of interrogating the activity of exopeptidases in the serum of cancer patients with the aim of establishing a distinction based on enzymatic function, instead of simple protein levels, as a means to biomarker discovery. We first analyzed two well-characterized mouse models of prostate cancer, each with a distinct genetic lesion, and established that broad exopeptidase and targeted aminopeptidase activity tests reveal proteolytic changes associated with tumor development. We also describe new peptide-based freeze-frame reagents uniquely suited to probe the altered balance of selected aminopeptidases, as opposed to the full array of exopeptidases, and/or their modulators in patient serum or plasma. One particular proteolytic activity was impaired in animals with aggressive disease relative to cancer-free littermates. We identified the protease in question as dipeptidyl peptidase 4 (DPP4) by analyzing selected knockout mice and evaluating the effect of specific inhibitors. DPP4 activity was also reduced in the sera of patients with metastatic prostate cancer relative to patients with localized disease or healthy controls. However, no significant differences in DPP4 serum levels were observed, which established the loss of activity as the result of impaired enzymatic function. Biochemical analysis indicated that reduced activity was the result not of post-translational modifications or allosteric changes, but instead of a low-molecular-weight inhibitor. After we adjusted for age and total prostate-specific antigen, reduced DPP4 activity remained a significant predictor of cancer status. The results of this proof-of-principle study suggest that DPP4 activity might be a potential blood-based indicator of the presence of metastatic cancer of prostatic origin, either by itself or, more likely, as a means to improve the sensitivity and specificity of existing markers.

Identification of potential serum peptide biomarkers of biliary tract cancer using MALDI MS profiling

BACKGROUND

The aim of this discovery study was the identification of peptide serum biomarkers for detecting biliary tract cancer (BTC) using samples from healthy volunteers and benign cases of biliary disease as control groups. This work was based on the hypothesis that cancer-specific exopeptidases exist and that their activities in serum can generate cancer-predictive peptide fragments from circulating proteins during coagulation.

METHODS

This case control study used a semi-automated platform incorporating polypeptide extraction linked to matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS) to profile 92 patient serum samples. Predictive models were generated to test a validation serum set from BTC cases and healthy volunteers.

RESULTS

Several peptide peaks were found that could significantly differentiate BTC patients from healthy controls and benign biliary disease. A predictive model resulted in a sensitivity of 100% and a specificity of 93.8% in detecting BTC in the validation set, whilst another model gave a sensitivity of 79.5% and a specificity of 83.9% in discriminating BTC from benign biliary disease samples in the training set. Discriminatory peaks were identified by tandem MS as fragments of abundant clotting proteins.

CONCLUSIONS

Serum MALDI MS peptide signatures can accurately discriminate patients with BTC from healthy volunteers.

Mapping O-glycosylation of apolipoprotein C-III in MALDI-FT-ICR protein profiles

Ultrahigh resolution MALDI-FT-ICR profiles were obtained from human serum samples that were processed using a fully automated RPC18-based magnetic bead method. Proteins were profiled from m/z value 6630 with a resolving power of 73 000 up to m/z value 12 600 with a resolving power of 37 000. In this study, a detailed evaluation was performed of the isoforms of apolipoprotein C-III, i.e. the different mucin-type core 1 O-glycans with the addition of one or two sialic acid residues. The MALDI-FT-ICR profiles are discussed with regard to reproducibility of the signal intensities as well as the accurate mass measurements. ESI-FT-ICR-MS/MS analyses of the same serum samples were performed to confirm the identity of apolipoprotein C-III glycoforms.

A prognostic biomarker for gastric cancer with lymph node metastases

Gastric cancer is one of the leading causes of tumor-related deaths in China. The tumor, node, metastasis (TNM) classification system is useful for predicting clinical prognosis of patients with gastric cancer. However, determining the presence of lymph node involvement in the early stages of gastric cancer is difficult without biopsy. Therefore, it is necessary to identify novel serum biomarkers for TNM cancer staging and prognostic follow-up. In this study, we have reported fibrinopeptide-A (FPA) with alanine truncation at the N-terminal as a novel biomarker to differentiate gastric cancer with and without lymph node metastases. We analyzed 369 individual serum samples including gastric cancer patients without lymph node metastases (n = 33), gastric cancer patients with lymph node metastases (n = 157; confirmed by pathology), and age- and sex-matched healthy individuals (n = 179). The data showed that 85.4% of patients with lymph node metastases were positive for FPA with alanine truncation at the N-terminal (degAla-FPA, 1,465.63 Da), as determined by tandem mass spectrometry (MS). Using degAla-FPA as the biomarker, the sensitivity was 85.4% for gastric cancer patients with lymph node metastases, and the specificity was 100% for gastric cancer patients without lymph node metastases. The high sensitivity and specificity achieved with serum degAla-FPA levels indicated that MS technology could facilitate the discovery of a novel and quantitative prognostic biomarker for gastric cancer with lymph node involvement.

Quantitative trait loci mapping of the mouse plasma proteome (pQTL)

A current challenge in the era of genome-wide studies is to determine the responsible genes and mechanisms underlying newly identified loci. Screening of the plasma proteome by high-throughput mass spectrometry (MALDI-TOF MS) is considered a promising approach for identification of metabolic and disease processes. Therefore, plasma proteome screening might be particularly useful for identifying responsible genes when combined with analysis of variation in the genome. Here, we describe a proteomic quantitative trait locus (pQTL) study of plasma proteome screens in an F(2) intercross of 455 mice mapped with 177 genetic markers across the genome. A total of 69 of 176 peptides revealed significant LOD scores (≥5.35) demonstrating strong genetic regulation of distinct components of the plasma proteome. Analyses were confirmed by mechanistic studies and MALDI-TOF/TOF, liquid chromatography-tandem mass spectrometry (LC-MS/MS) analyses of the two strongest pQTLs: A pQTL for mass-to-charge ratio (m/z) 3494 (LOD 24.9, D11Mit151) was identified as the N-terminal 35 amino acids of hemoglobin subunit A (Hba) and caused by genetic variation in Hba. Another pQTL for m/z 8713 (LOD 36.4; D1Mit111) was caused by variation in apolipoprotein A2 (Apoa2) and cosegregated with HDL cholesterol. Taken together, we show that genome-wide plasma proteome profiling in combination with genome-wide genetic screening aids in the identification of causal genetic variants affecting abundance of plasma proteins.

Profiling plasma peptides for the identification of potential ageing biomarkers in Chinese Han adults

Advancing age is associated with cardiovascular disease, diabetes mellitus and cancer, and shows significant inter-individual variability. To identify ageing-related biomarkers we performed a proteomic analysis on 1890 Chinese Han individuals, 1136 males and 754 females, aged 18 to 82 years, using weak cation exchange magnetic bead based MALDI-TOF-MS analysis. The study identified 44 peptides which varied in concentration in different age groups. In particular, apolipoprotein A-I (ApoA1) concentration gradually increased between 18 to 50 years of age, the levels of fibrinogen alpha (FGA) decreased over the same age span, while albumin (ALB) was significantly degraded in middle-aged individuals. In addition, the plasma peptide profiles of FGA and four other unidentified proteins were found to be gender-dependent. Plasma proteins such as FGA, ALB and ApoA1 are significantly correlated with age in the Chinese Han population and could be employed as indicative ageing-related biomarkers.

Reproducibility of mass spectrometry based protein profiles for diagnosis of ovarian cancer across clinical studies: A systematic review

The focus of this systematic review is to give an overview of the current status of clinical protein profiling studies using MALDI and SELDI MS platforms in the search for ovarian cancer biomarkers. A total of 34 profiling studies were qualified for inclusion in the review. Comparative analysis of published discriminatory peaks to peaks found in an original MALDI MS protein profiling study was made to address the key question of reproducibility across studies. An overlap was found despite substantial heterogeneity between studies relating to study design, biological material, pre-analytical treatment, and data analysis. About 47% of the peaks reported to be associated to ovarian cancer were also represented in our experimental study, and 34% of these redetected peaks also showed a significant difference between cases and controls in our study. Thus, despite known problems related to reproducibility an overlap in peaks between clinical studies was demonstrated, which indicate convergence toward a set of common discriminating, reproducible peaks for ovarian cancer. The potential of the discriminating protein peaks for clinical use as ovarian cancer biomarkers will be discussed and evaluated. This article is part of a Special Issue entitled: Proteomics: The clinical link.

MS/MS library facilitated MRM quantification of native peptides prepared by denaturing ultrafiltration

Naturally occurring native peptides provide important information about physiological states of an organism and its changes in disease conditions but protocols and methods for assessing their abundance are not well-developed. In this paper, we describe a simple procedure for the quantification of non-tryptic peptides in body fluids. The workflow includes an enrichment step followed by two-dimensional fractionation of native peptides and MS/MS data management facilitating the design and validation of LC- MRM MS assays. The added value of the workflow is demonstrated in the development of a triplex LC-MRM MS assay used for quantification of peptides potentially associated with the progression of liver disease to hepatocellular carcinoma.

Precision profiling and identification of human serum peptides using Fourier transform ion cyclotron resonance mass spectrometry

Many biomarker discovery studies are based on matrix-assisted laser desorption/ionisation (MALDI) peptide profiles. In this study, 96 human serum samples were analysed on a Bruker solariX(TM) MALDI Fourier transform ion cyclotron resonance (FTICR) system equipped with a 15 tesla magnet. Isotopically resolved peptides were observed in ultrahigh resolution FTICR profiles up to m/z 6500 with mass measurement errors (MMEs) of previously identified peptides at a sub-ppm level. For comparison with our previous platform for peptide profile mass analysis (i.e. Ultraflex II) the corresponding time-of-flight (TOF) spectra were obtained with isotopically resolved peptides up to m/z 3500. The FTICR and TOF systems performed rather similar with respect to the repeatability of the signal intensities. However, the mass measurement precision improved at least 10-fold in ultrahigh resolution data and thus simplified spectral alignment necessary for robust and quantitatively precise comparisons of profiles in large-scale clinical studies. From each single MALDI-FTICR spectrum an m/z-list was obtained with sub-ppm precision for all different species, which is beneficial for identification purposes and interlaboratory comparisons. Furthermore, the FTICR system allowed new peptide identifications from collision-induced dissociation (CID) spectra using direct infusion of reversed-phase (RP) C(18)-fractionated serum samples on an electrospray ionisation (ESI) source.

A high performance profile-biomarker diagnosis for mass spectral profiles

BACKGROUND

Although mass spectrometry based proteomics demonstrates an exciting promise in complex diseases diagnosis, it remains an important research field rather than an applicable clinical routine for its diagnostic accuracy and data reproducibility. Relatively less investigation has been done yet in attaining high-performance proteomic pattern classification compared with the amount of endeavours in enhancing data reproducibility.

METHODS

In this study, we present a novel machine learning approach to achieve a clinical level disease diagnosis for mass spectral data. We propose multi-resolution independent component analysis, a novel feature selection algorithm to tackle the large dimensionality of mass spectra, by following our local and global feature selection framework. We also develop high-performance classifiers by embedding multi-resolution independent component analysis in linear discriminant analysis and support vector machines.

RESULTS

Our multi-resolution independent component based support vector machines not only achieve clinical level classification accuracy, but also overcome the weakness in traditional peak-selection based biomarker discovery. In addition to rigorous theoretical analysis, we demonstrate our method's superiority by comparing it with nine state-of-the-art classification and regression algorithms on six heterogeneous mass spectral profiles.

CONCLUSIONS

Our work not only suggests an alternative direction from machine learning to accelerate mass spectral proteomic technologies into a clinical routine by treating an input profile as a 'profile-biomarker', but also has positive impacts on large scale 'omics' data mining. Related source codes and data sets can be found at: https://sites.google.com/site/heyaumbioinformatics/home/proteomics.

Proteomic technologies for the identification of disease biomarkers in serum: advances and challenges ahead

Serum is an ideal biological sample that contains an archive of information due to the presence of a variety of proteins released by diseased tissue, and serum proteomics has gained considerable interest for the disease biomarker discovery. Easy accessibility and rapid protein changes in response to disease pathogenesis makes serum an attractive sample for clinical research. Despite these advantages, the analysis of serum proteome is very challenging due to the wide dynamic range of proteins, difficulty in finding low-abundance target analytes due to the presence of high-abundance serum proteins, high levels of salts and other interfering compounds, variations among individuals and paucity of reproducibility. Sample preparation introduces pre-analytical variations and poses major challenges to analyze the serum proteome. The label-free detection techniques such as surface plasmon resonance, microcantilever, few nanotechniques and different resonators are rapidly emerging for the analysis of serum proteome and they have exhibited potential to overcome few limitations of the conventional techniques. In this article, we will discuss the current status of serum proteome analysis for the biomarker discovery and address key technological advancements, with a focus on challenges and amenable solutions.

Serum apolipoproteins C-I and C-III are reduced in stomach cancer patients: results from MALDI-based peptidome and immuno-based clinical assays

Finding new peptide biomarkers for stomach cancer in human sera that can be implemented into a clinically practicable prediction method for monitoring of stomach cancer. We studied the serum peptidome from two different biorepositories. We first employed a C8-reverse phase liquid chromatography approach for sample purification, followed by mass-spectrometry analysis. These were applied onto serum samples from cancer-free controls and stomach cancer patients at various clinical stages. We then created a bioinformatics analysis pipeline and identified peptide signature discriminating stomach adenocarcinoma patients from cancer-free controls. Matrix Assisted Laser Desorption/Ionization–Time of Flight (MALDI-TOF) results from 103 samples revealed 9 signature peptides; with prediction accuracy of 89% in the training set and 88% in the validation set. Three of the discriminating peptides discovered were fragments of Apolipoproteins C-I and C-III (apoC-I and C-III); we further quantified their serum levels, as well as CA19-9 and CRP, employing quantitative commercial-clinical assays in 142 samples. ApoC-I and apoC-III quantitative results correlated with the MS results. We then employed apoB-100-normalized apoC-I and apoC-III, CA19-9 and CRP levels to generate rules set for stomach cancer prediction. For training, we used sera from one repository, and for validation, we used sera from the second repository. Prediction accuracies of 88.4% and 74.4% were obtained in the training and validation sets, respectively. Serum levels of apoC-I and apoC-III combined with other clinical parameters can serve as a basis for the formulation of a diagnostic score for stomach cancer patients.

MALDI-TOF serum profiling using semiautomated serum peptide capture with magnetic reversed phase (C18) beads

Mass spectrometry can be used to generate diagnostic peptide peak profiles "signatures" of serum samples. Peak profiles can be used to compare different sera and correlate samples (i.e., patient groups) with clinical data to assist in diagnosis, monitoring, and/or prediction. We describe the semiautomated capture of serum peptides/small proteins with magnetic beads that harbor C18 alkyl chains, the deposition of captured material on a target plate for MALDI-TOF mass spectrometry, as well as, general guidelines for data acquisition. We also include, in a separate note, a short manual version of the capture procedure. Overall, the serum sample processing protocol, reported here, is reproducible and robust. In conjunction with MALDI-TOF mass spectrometry, this protocol allows for the profiling of several hundreds of serum peptides.

Standardized peptidome profiling of human serum for the detection of pancreatic cancer

OBJECTIVE

To establish new biomarkers for accurate diagnosis of pancreatic cancer (PC) using a standardized serum peptidome profiling and compare the results with those from the tumor marker, CA 19-9.

METHODS

Serum samples from 102 patients (55 with chronic pancreatitis and 47 with PC) and 56 healthy controls were collected and analyzed following a protocol that was rigorously designed to prevent preanalytical variation. Serum peptides were extracted using immobilized copper ion chromatography on a robotic platform. Mass spectra were acquired by matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry on an Autoflex II spectrometer (Bruker Daltonics, Bremen, Germany). Statistical analysis was performed using the Clinprotools 2.2 software (Bruker Daltonics) and the SPSS 15.0 software (SPSS Inc, Chicago, Ill).

RESULTS

Standardized peptidome profiling showed a median coefficient of variation of 11.6% calculated using all the extracted peptides and negligible influence of sex and age on peptidome profiles. The diagnostic sensitivity was 89.9%, and the diagnostic specificity was 92.7%, using 2 serum features and CA 19-9 serum concentration. Healthy controls were differentiated from patients with PC and chronic pancreatitis, with the use of 3 features of the peptidome (diagnostic sensitivity, 98.2%; diagnostic specificity, 97.1%).

CONCLUSIONS

Standardized serum peptidome profiling could be a useful tool to improve biochemical diagnosis of PC in combination with the classic tumor marker, CA 19-9.

Quality control based on isotopic distributions for high-throughput MALDI-TOF and MALDI-FTICR serum peptide profiling

In this study, we have implemented a new quality control (QC) parameter for peptide profiling based on isotopic distributions. This QC parameter is an objective measure and facilitates automatic sorting of large numbers of peptide spectra. Peptides in human serum samples were enriched using reversed-phase C(18)-functionalized magnetic beads using a high-throughput robotic platform. High-resolution MALDI-TOF and ultrahigh resolution MALDI-FTICR mass spectra were obtained and a workflow was developed for automated analysis and evaluation of these profiles. To this end, the isotopic distributions of multiple peptides were quantified from both MALDI-TOF and MALDI-FTICR spectra. Odd peptide isotope distributions in TOF spectra could be rationalized from ultrahigh resolution FTICR spectra that showed overlap of different peptides. The comparison of isotope patterns with estimated polyaveragine distributions was used to calculate a QC value for each single mass spectrum. Sorting these QC values enabled the best MALDI spectrum to be selected from replicate spots. Moreover, using this approach spectra containing high intensities of polymers or other contaminants and lacking peptides of interest can be efficiently removed from a clinical dataset. In general, this method simplifies the exclusion of low quality spectra from further statistical analysis.

Identification of a uromodulin fragment for diagnosis of IgA nephropathy

IgA nephropathy (IgAN) is one of the most common types of glomerulonephritis worldwide and is diagnosed only with a renal biopsy. The purpose of the present studies was to identify the potential biomarkers for the non-invasive diagnosis of IgAN. The combination of a magnetic bead separation system with matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) was used to analyze urinary peptides of IgAN patients, other glomerulopathy patients, and healthy controls. ClinProTools v.2.0 software was also applied to establish a diagnostic model for IgAN. Our results demonstrated that 11 features had optimal discriminatory performance (p <0.00001). Among these features, the peptide with m/z 1913.14 was identified as a fragment of uromodulin. Receiver operating characteristic (ROC) analysis for m/z 1913.14 showed that the area under the curve (AUC) was 0.998 for distinguishing IgAN versus healthy controls, and 0.815 for distinguishing IgAN versus other glomerulopathy. Analysis of urine peptides patterns by the magnetic bead separation system and MALDI-TOF-MS was a non-invasive diagnostic tool. We conclude that the urinary peptide with m/z 1913.14, which was identified as a uromodulin fragment, may be used as a biomarker for the non-invasive diagnosis of IgAN clinically.

Increased circulating cell signalling phosphoproteins in sera are useful for the detection of pancreatic cancer

BACKGROUND

Intracellular phosphoprotein activation significantly regulates cancer progression. However, the significance of circulating phosphoproteins in the blood remains unknown. We investigated the serum phosphoprotein profile involved in pancreatic cancer (PaCa) by a novel approach that comprehensively measured serum phosphoproteins levels, and clinically applied this method to the detection of PaCa.

METHODS

We analysed the serum phosphoproteins that comprised cancer cellular signal pathways by comparing sera from PaCa patients and benign controls including healthy volunteers (HVs) and pancreatitis patients.

RESULTS

Hierarchical clustering analysis between PaCa patients and HVs revealed differential pathway-specific profiles. In particular, the components of the extracellular signal-regulated kinase (ERK) signalling pathway were significantly increased in the sera of PaCa patients compared with HVs. The positive rate of p-ERK1/2 (82%) was found to be superior to that of CA19-9 (53%) for early stage PaCa. For the combination of these serum levels, the area under the receiver-operator characteristics curves was showing significant ability to distinguish between the two populations in independent validation set, and between cancer and non-cancer populations in another validation set.

CONCLUSION

The comprehensive measurement of serum cell signal phosphoproteins is useful for the detection of PaCa. Further investigations will lead to the implementation of tailor-made molecular-targeted therapeutics.

High-throughput and targeted in-depth mass spectrometry-based approaches for biofluid profiling and biomarker discovery

Proteomics aims to create a link between genomic information, biological function and disease through global studies of protein expression, modification and protein-protein interactions. Recent advances in key proteomics tools, such as mass spectrometry (MS) and (bio)informatics, provide tremendous opportunities for biomarker-related clinical applications. In this review, we focus on two complementary MS-based approaches with high potential for the discovery of biomarker patterns and low-abundant candidate biomarkers in biofluids: high-throughput matrix-assisted laser desorption/ionization time-of-flight mass spectroscopy-based methods for peptidome profiling and label-free liquid chromatography-based methods coupled to MS for in-depth profiling of biofluids with a focus on subproteomes, including the low-molecular-weight proteome, carrier-bound proteome and N-linked glycoproteome. The two approaches differ in their aims, throughput and sensitivity. We discuss recent progress and challenges in the analysis of plasma/serum and proximal fluids using these strategies and highlight the potential of liquid chromatography-MS-based proteomics of cancer cell and tumor secretomes for the discovery of candidate blood-based biomarkers. Strategies for candidate validation are also described.

Protein mass spectra data analysis for clinical biomarker discovery: a global review

The identification of new diagnostic or prognostic biomarkers is one of the main aims of clinical cancer research. In recent years there has been a growing interest in using high throughput technologies for the detection of such biomarkers. In particular, mass spectrometry appears as an exciting tool with great potential. However, to extract any benefit from the massive potential of clinical proteomic studies, appropriate methods, improvement and validation are required. To better understand the key statistical points involved with such studies, this review presents the main data analysis steps of protein mass spectra data analysis, from the pre-processing of the data to the identification and validation of biomarkers.

Serum peptidome patterns of human systemic lupus erythematosus based on magnetic bead separation and MALDI-TOF mass spectrometry analysis

OBJECTIVES

Currently few studies have been reported to utilize matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) in rheumatic disease, especially in systemic lupus erythematosus (SLE). Our aim was to find differentially expressed disease-related and condition-specific peptides in sera from patients with SLE, as well as to develop and validate the peptide classification model for the diagnosis of SLE.

METHODS

Based on the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) 2000, 50 SLE patients were divided into two subgroups: 25 were defined as stable SLE (SLEDAI < or = 8) and 25 as active SLE (SLEDAI > 8). Twenty-five patients with rheumatoid arthritis (RA) and 24 healthy donors were also included and underwent analysis. We performed magnetic beads-based weak cation exchange chromatography (MB-WCX) for sample processing and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) for peptide profiling. ClinProt software 2.1 was used for data analysis and a genetic algorithm was modelled for class prediction.

RESULTS

A series of significant short peptides was detected. Classification models were developed to classify samples across normal controls, active SLE patients, and stable SLE patients, and achieved high capability of prediction and cross-validation. Blinded verification of the classification model showed 91.7% sensitivity in active SLE, 83.3% sensitivity in stable SLE, and 86.7% specificity in normal controls.

CONCLUSION

We have completed a preliminary study to describe the serum peptide profile of SLE and improve the diagnosis of SLE from an integrated perspective of peptide mass patterns.

Peptides generated ex vivo from serum proteins by tumor-specific exopeptidases are not useful biomarkers in ovarian cancer

BACKGROUND

The serum peptidome may be a valuable source of diagnostic cancer biomarkers. Previous mass spectrometry (MS) studies have suggested that groups of related peptides discriminatory for different cancer types are generated ex vivo from abundant serum proteins by tumor-specific exopeptidases. We tested 2 complementary serum profiling strategies to see if similar peptides could be found that discriminate ovarian cancer from benign cases and healthy controls.

METHODS

We subjected identically collected and processed serum samples from healthy volunteers and patients to automated polypeptide extraction on octadecylsilane-coated magnetic beads and separately on ZipTips before MALDI-TOF MS profiling at 2 centers. The 2 platforms were compared and case control profiling data analyzed to find altered MS peak intensities. We tested models built from training datasets for both methods for their ability to classify a blinded test set.

RESULTS

Both profiling platforms had CVs of approximately 15% and could be applied for high-throughput analysis of clinical samples. The 2 methods generated overlapping peptide profiles, with some differences in peak intensity in different mass regions. In cross-validation, models from training data gave diagnostic accuracies up to 87% for discriminating malignant ovarian cancer from healthy controls and up to 81% for discriminating malignant from benign samples. Diagnostic accuracies up to 71% (malignant vs healthy) and up to 65% (malignant vs benign) were obtained when the models were validated on the blinded test set.

CONCLUSIONS

For ovarian cancer, altered MALDI-TOF MS peptide profiles alone cannot be used for accurate diagnoses.

Detection of lung cancer using plasma protein profiling by matrix-assisted laser desorption/ionization mass spectrometry

There are no satisfactory plasma biomarkers which are available for the early detection and monitoring of lung cancer, one of the most frequent cancers worldwide. The aim of this study is to explore the application of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-ToF MS) to plasma proteomic patterns to distinguish lung cancer patients from healthy individuals. The EDTA plasma samples have been pre-fractionated using magnetic bead kits functionalized with weak cation exchange coatings. We compiled MS protein profiles for 90 patients with squamous cell carcinomas (SCC) and compared them with profiles from 187 healthy controls. The MALDI-ToF spectra were analyzed statistically using ClinProTools bioinformatics software. Depending on the sample used, up to 441 peaks/spectrum could be detected in a mass range of 1000-20,000 Da; 33 of these proteins had statistically differential expression levels between SCC and control plasma (P < 0.001). The series of the peaks were automatically chosen as potential biomarker patterns in the training set. They allowed the discrimination of plasma samples from healthy control and samples from SCC patients (sensitivity and specificity >90%) in external validation test. These results suggest that plasma MALDI-ToF MS protein profiling can distinguish patients with SCC and also from healthy individuals with relatively high sensitivity and specificity and that MALDI- ToF MS is a potential tool for the screening of lung cancer.

Perspectives of targeted mass spectrometry for protein biomarker verification

The identification of specific biomarkers will improve the early diagnosis of disease, facilitate the development of targeted therapies, and provide an accurate method to monitor treatment response. A major challenge in the process of verifying biomarker candidates in blood plasma is the complexity and high dynamic range of proteins. This article reviews the current, targeted proteomic strategies that are capable of quantifying biomarker candidates at concentration ranges where biomarkers are expected in plasma (i.e. at the ng/ml level). In addition, a workflow is presented that allows the fast and definitive generation of targeted mass spectrometry-based assays for most biomarker candidate proteins. These assays are stored in publicly accessible databases and have the potential to greatly impact the throughput of biomarker verification studies.

Monitoring peptidase activities in complex proteomes by MALDI-TOF mass spectrometry

Measuring enzymatic activities in biological fluids is a form of activity-based proteomics and may be utilized as a means of developing disease biomarkers. Activity-based assays allow amplification of output signals, thus potentially visualizing low-abundant enzymes on a virtually transparent whole-proteome background. The protocol presented here describes a semiquantitative in vitro assay of proteolytic activities in complex proteomes by monitoring breakdown of designer peptide substrates using robotic extraction and a matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometric readout. Relative quantitation of the peptide metabolites is carried out by comparison with spiked internal standards, followed by statistical analysis of the resulting mini-peptidome. Partial automation provides reproducibility and throughput essential for comparing large sample sets. The approach may be used for diagnostic or predictive purposes and it enables profiling of 96 samples in 30 h. It could be tailored to many diagnostic and pharmaco-dynamic purposes as a readout of catalytic and metabolic activities in body fluids or tissues.

Study of the human plasma proteome of rheumatoid arthritis

In this study, we report a combined proteomic and peptidomic analysis of human plasma from patients with rheumatoid arthritis (RA) and controls. We used molecular weight cut-off filters (MWCO: 10kDa) to enrich low-molecular-weight (LMW) peptides from human plasma. The peptide fraction was analyzed without trypsin digestion by capillary reversed-phase high-performance liquid chromatography (HPLC) coupled to a linear ion trap-FT-MS system, which identified 771 unique peptides in the peptidome study (from 145 protein progenitors). An anti-albumin/anti-IgG column was used to remove albumin and immunoglobulin G (IgG) from the human plasma. After that, the albumin/IgG-depleted sample was fractionated into a bound fraction and an unbound fraction on a multi-lectin affinity column (M-LAC). LC-MS analysis of the corresponding tryptic digests identified 308 proteins using the M-LAC approach. Relative differences in the following protein classifications were observed in the RA human plasma samples compared with controls: structural proteins, immuno-related proteins, protease inhibitors, coagulation proteins, transport proteins and apolipoproteins. While some of these proteins/peptides have been previously reported to be associated with RA disease such as calgranulin A, B, C and C-reactive protein, several others were newly identified (such as thymosin beta4, actin, tubulin, and vimentin), which may further the understanding of the disease pathogenesis. Moreover, we have found that the peptidomic and glycoproteomic approaches were complementary and allow a more complete picture of the human plasma proteome which can be valuable in studies of disease etiology.

Serum proteomics using mass spectrometry

The identification and eventual application of tumor markers in cancer screening, early detection, diagnosis, and prognosis is a continuing focus of significant translational cancer research. While many new candidate markers have been discovered and at least partly characterized, very few have found widespread clinical application limited presently to the use of CA-125 in ovarian cancer, CEA, primarily in colon cancer, and PSA in prostate cancer screening and patient monitoring. The rapidly emerging field of cancer genomics and proteomics, and their clinical translation as "molecular diagnosis" and "molecular medicine" are already beginning to transform the field, and the accelerating growth of information and technology in this research area will undoubtedly transform the field of tumor markers and their application in the near future leading to improved molecular tools for cancer diagnosis, prognosis, and treatment and ultimately, to the emergence of novel and more effective cancer therapies, including improved approaches for immunotherapy and cancer prevention strategies. Toward this goal, herein are described detailed methods and workflows for mass spectrometry-based biomarker discovery in serum/plasma utilizing two complementary approaches - matrix-assisted laser desorption ionization time of flight (MALDI-TOF) and nanoflow reversed-phase liquid chromatography (RPLC)-tandem mass spectrometry (MS/MS). These discovery workflows incorporate both abundant protein depletion and sample fractionation upstream of analytical mass spectrometry to optimize the identification and quantitation of lower abundant species.