Comprehensive multiplexed autoantibody profiling of patients with advanced urothelial cancer.

BACKGROUND: Comprehensive profiling of autoantibodies (AAbs) in metastatic urothelial cancer (mUC) has not been performed to date. This may aid in diagnosis of UC, uncover novel therapeutic targets in this disease as well as identify associations between AAbs and response and toxicity to systemic therapies. METHODS: We used serum from patients with mUC collected prior to and after systemic therapy (immune checkpoint inhibitor (ICI) or platinum-based chemotherapy (PBC)) at Dana-Farber Cancer Institute. 38 age-matched and sex-matched healthy controls (HCs) from healthy blood donors were also evaluated. The SeroTag immuno-oncology discovery array (Oncimmune) was used, with quantification of the AAb reactivity toward 1132 antigens. Bound AAbs were detected using an anti-immunoglobulin G-specific detection antibody conjugated to the fluorescent reporter dye phycoerythrin. The AAb reactivity was reported as the median fluorescence intensity for each color and sample using a Luminex FlexMAP3D analyzer. Clinical outcomes of interest included radiographic response and development of immune-related adverse events (irAEs). Significance analysis of microarray was used to compare mUC versus HC and radiographic response. Associations with irAE were evaluated using a logistic regression model. P<0.05 was considered statistically significant. RESULTS: 66 patients were included with a median age of 68 years; 54 patients (82%) received ICI and 12 patients (18%) received PBC. Compared with HCs, AAbs against the cancer/testis antigens (CTAG1B, CTAG2, MAGEB18), HSPA1A, TP53, KRAS, and FGFR3 were significantly elevated in patients with mUC. AAbs against BRCA2, TP53, and CTNBB1 were associated with response, and those against BICD2 and UACA were associated with resistance to ICI therapy. AAbs against MITF, CDH3, and KDM4A were associated with development of irAEs in patient who received an ICI. A higher variance in pre-to-post treatment fold change in AAb levels was seen in patients treated with ICI versus PBC and was associated with response to ICI. CONCLUSIONS: This is the first report of comprehensive AAb profiling of patients with mUC and identified key AAbs that were elevated in patients with mUC versus HCs as well as AAbs associated with therapeutic response to ICI. These findings are hypothesis generating and further mechanistic studies evaluating humoral immunity in UC are required.

Paradoxical sex-specific patterns of autoantibody response to SARS-CoV-2 infection.

BACKGROUND: Pronounced sex differences in the susceptibility and response to SARS-CoV-2 infection remain poorly understood. Emerging evidence has highlighted the potential importance of autoimmune activation in modulating the acute response and recovery trajectories following SARS-CoV-2 exposure. Given that immune-inflammatory activity can be sex-biased in the setting of severe COVID-19 illness, the aim of the study was to examine sex-specific autoimmune reactivity to SARS-CoV-2 in the absence of extreme clinical disease. METHODS: In this study, we assessed autoantibody (AAB) reactivity to 91 autoantigens previously linked to a range of classic autoimmune diseases in a cohort of 177 participants (65% women, 35% men, mean age of 35) with confirmed evidence of prior SARS-CoV-2 infection based on presence of antibody to the nucleocapsid protein of SARS-CoV-2. Data were compared to 53 pre-pandemic healthy controls (49% women, 51% men). For each participant, socio-demographic data, serological analyses, SARS-CoV-2 infection status and COVID-19 related symptoms were collected by  an electronic survey of questions. The symptoms burden score was constructed based on the total number of reported symptoms (N = 21) experienced within 6 months prior to the blood draw, wherein a greater number of symptoms corresponded to a higher score and assigned as more severe burden. RESULTS: In multivariable analyses, we observed sex-specific patterns of autoreactivity associated with the presence or absence (as well as timing and clustering of symptoms) associated with prior COVID-19 illness. Whereas the overall AAB response was more prominent in women following asymptomatic infection, the breadth and extent of AAB reactivity was more prominent in men following at least mildly symptomatic infection. Notably, the observed reactivity included distinct antigens with molecular homology with SARS-CoV-2. CONCLUSION: Our results reveal that prior SARS-CoV-2 infection, even in the absence of severe clinical disease, can lead to a broad AAB response that exhibits sex-specific patterns of prevalence and antigen selectivity. Further understanding of the nature of triggered AAB activation among men and women exposed to SARS-CoV-2 will be essential for developing effective interventions against immune-mediated sequelae of COVID-19.

Profiling of IgG antibodies targeting unmodified and corresponding citrullinated autoantigens in a multicenter national cohort of early arthritis in Germany.

OBJECTIVE: To assess the diagnostic potential of IgG antibodies to citrullinated and corresponding native autoantigens in early arthritis. METHODS: IgG autoantibodies to 390 distinct unmodified and corresponding in vitro citrullinated recombinant proteins were measured by a multiplex assay in baseline blood samples from a German multicenter national cohort of 411 early arthritis patients (56.5 ± 14.6 years, 62.8% female). The cohort was randomly split into a training cohort (n = 329, 28.6% ACPA positive) and a validation cohort (n = 82, 32.9% ACPA pos.). The diagnostic properties of candidate antibodies to predict a subsequent diagnosis of rheumatoid arthritis (RA) as opposed to a non-RA diagnosis were assessed by receiver operating characteristics analysis and generalized linear modeling (GLM) with Bonferroni correction in comparison to clinically determined IgM rheumatoid factor (RF) and citrullinated peptide antibody (ACPA) status. RESULTS: Of 411 patients, 309 (75.2%) were classified as RA. Detection rates of antibody responses to citrullinated and uncitrullinated forms of the proteins were weakly correlated (Spearman's r = 0.13 (95% CI 0.029-0.22), p = 0.01). The concentration of 34 autoantibodies (32 to citrullinated and 2 to uncitrullinated antigens) was increased at least 2-fold in RA patients and further assessed. In the training cohort, a significant association of citrullinated "transformer 2 beta homolog" (cTRA2B)-IgG with RA was observed (OR 5.3 × 103, 95% CI 0.8 × 103-3.0 × 106, p = 0.047). Sensitivity and specificity of cTRA2B-IgG (51.0%/82.9%) were comparable to RF (30.8%/91.6%) or ACPA (32.1%/94.7%). Similar results were obtained in the validation cohort. The addition of cTRA2B-IgG to ACPA improved the diagnostic performance over ACPA alone (p = 0.026 by likelihood ratio test). CONCLUSIONS: cTRA2B-IgG has the potential to improve RA diagnosis in conjunction with RF and ACPA in early arthritis.

Proteomic analysis to define predictors of treatment response to adalimumab or methotrexate in rheumatoid arthritis patients.

Seropositivity for anti-citrullinated peptide antibodies (ACPA) in patients with rheumatoid arthritis (RA), a chronic autoimmune arthritis, is associated with worse long-term disease outcomes. ACPA is ubiquitously tested in RA patients, but other autoantibodies exist (in both citrullinated and non-citrullinated form) which may provide additional information on RA subtypes and/or treatment response. We used a multiplex bead-based assay of 376 autoantibodies to test associations between these autoantibodies and treatment response in RA patients. Clusters of patients with similar autoantibody expression were defined and cluster membership was associated with treatment response. Thirty-four autoantibodies were differentially expressed in RA patients compared with healthy controls; citrullinated vimentin was associated with treatment response. A selection of citrullinated autoantibodies was found to be associated with treatment response in a subanalysis of ACPA-negative RA patients. Finer ACPA specificities in ACPA-negative RA patients may be predictive of treatment response and could represent a rich vein of future study.

Comprehensive Longitudinal Surveillance of the IgG Autoantibody Repertoire in Established Systemic Lupus Erythematosus.

OBJECTIVE: To investigate the role of epitope spreading in established systemic lupus erythematosus (SLE). METHODS: IgG autoantibody reactivity with 398 distinct recombinant proteins was measured over a period of 6 years in 69 SLE patients and compared to that in 45 controls. Changes in mean fluorescence intensity (MFI), number of autoantibodies to distinct antigens, and reactivity with distinct clones of established antigenic targets (e.g., U1 RNP, Sm, and ribosomal P) representing epitope fine mapping were assessed. Linear mixed modeling, adjusted with Bonferroni correction for age and sex, was applied. RESULTS: The total number of autoantibodies, mean MFI, and number of autoantibodies in epitope fine mapping were higher in SLE patients compared to controls (P < 0.0001). The total number of antibodies to distinct autoantigens remained stable over time, while the mean MFI decreased over time in SLE (P < 0.021). SLE patients showed variable recognition of epitopes in fine mapping over time. In particular, in SLE patients, more clones of the U1 RNP complex were recognized at the time of new organ involvement (+0.65) (P = 0.007). Mean MFI was higher in patients with lupus nephritis (P = 0.047). The time-averaged MFIs of 22 individual autoantibodies (including double-stranded DNA [dsDNA]) were higher, after Bonferroni correction, in SLE (P < 0.0001). The MFIs of dsDNA and histone cluster 2 H3c were associated with scores on the Systemic Lupus Activity Measure (P < 0.0001). CONCLUSION: Longitudinal surveillance of the IgG autoantibody repertoire in established SLE reveals evidence of sustained breadth of autoantibody repertoire without significant expansion. Associations of disease activity with dsDNA and with histone H3 autoantibodies were confirmed.

High diagnostic accuracy of histone H4-IgG autoantibodies in systemic lupus erythematosus.

Objective: Diagnosis of SLE relies on the detection of autoantibodies. We aimed to assess the diagnostic potential of histone H4 and H2A variant antibodies in SLE. Methods: IgG-autoantibodies to histones H4 (HIST1H4A), H2A type 2-A (HIST2H2AA3) and H2A type 2-C (HIST2H2AC) were measured along with a standard antibody (SA) set including SSA, SSB, Sm, U1-RNP and RPLP2 in a multiplex magnetic microsphere-based assay in 153 SLE patients [85% female, 41 (13.5) years] and 81 healthy controls [77% female, 43.3 (12.4) years]. Receiver operating characteristic analysis was performed to assess diagnostic performance of individual markers. Logistic regression analysis was performed on a random split of samples to determine the additional value of histone antibodies in comparison with SA by likelihood ratio test and determination of diagnostic accuracy in the remaining validation samples. Results: Microsphere-based assay showed good interclass correlation (mean 0.85, range 0.73-0.99) and diagnostic performance in receiver operating characteristic analysis (area under the curve (AUC) range 84.8-93.2) compared with routine assay for SA parameters. HIST1H4A-IgG was the marker with the best individual diagnostic performance for SLE vs healthy (AUC 0.97, sensitivity 95% at 90% specificity). HIST1H4A-IgG was an independent significant predictor for the diagnosis of SLE in multivariate modelling (P < 0.0001), and significantly improved prediction of SLE over SA parameters alone (residual deviance 45.9 vs 97.1, P = 4.3 × 10-11). Diagnostic accuracy in the training and validation samples was 89 and 86% for SA, and 95 and 89% with the addition of HIST1H4A-IgG. Conclusion: HIST1H4A-IgG antibodies improve diagnostic accuracy for SLE vs healthy.

Sequential high-content profiling of the IgG-autoantibody repertoire reveals novel antigens in rheumatoid arthritis.

BACKGROUND: The aim was to identify novel diagnostic autoantibody candidates for rheumatoid arthritis (RA) by comprehensive screening for autoreactivity. METHOD: We incubated 5892 recombinant proteins coupled to fluorescent beads, with patients' sera for the detection of IgG-autoantibodies in three independent patient cohorts: A (n = 72 patients with established RA); B/B- (n = 116 patients with early RA (B) and n = 51 CCP-negative patients with early RA from B (B-)); and C (n = 184 patients with early seronegative RA), in comparison to matched healthy controls. Intersects of significantly increased autoantibodies as determined by the Mann-Whitney test were sought. RESULT: Screening of 5892 antigens in RA cohorts A and B, or the seronegative cohorts B- and C revealed intersects of 23 and 13 significantly increased autoantibodies, respectively. Reactivity to three antigens was increased in all cohorts tested: N-acetylglucosamine-1-phosphate transferase, gamma subunit (GNPTG), heterogeneous nuclear ribonucleoprotein A1-like 2 (HNRNPA1), and insulin-like growth factor binding protein 2 (IGFBP2). CONCLUSIONS: Comprehensive sequential screening for autoantibodies reveals novel candidates for diagnostic markers in both seropositive and seronegative RA and suggests new fields of research into the pathogenesis of RA.

Comparison of pre-processing methods for multiplex bead-based immunoassays.

BACKGROUND: High throughput protein expression studies can be performed using bead-based protein immunoassays, such as the Luminex® xMAP® technology. Technical variability is inherent to these experiments and may lead to systematic bias and reduced power. To reduce technical variability, data pre-processing is performed. However, no recommendations exist for the pre-processing of Luminex® xMAP® data. RESULTS: We compared 37 different data pre-processing combinations of transformation and normalization methods in 42 samples on 384 analytes obtained from a multiplex immunoassay based on the Luminex® xMAP® technology. We evaluated the performance of each pre-processing approach with 6 different performance criteria. Three performance criteria were plots. All plots were evaluated by 15 independent and blinded readers. Four different combinations of transformation and normalization methods performed well as pre-processing procedure for this bead-based protein immunoassay. CONCLUSIONS: The following combinations of transformation and normalization were suitable for pre-processing Luminex® xMAP® data in this study: weighted Box-Cox followed by quantile or robust spline normalization (rsn), asinh transformation followed by loess normalization and Box-Cox followed by rsn.

Plasma proteins predict conversion to dementia from prodromal disease.

BACKGROUND: The study aimed to validate previously discovered plasma biomarkers associated with AD, using a design based on imaging measures as surrogate for disease severity and assess their prognostic value in predicting conversion to dementia. METHODS: Three multicenter cohorts of cognitively healthy elderly, mild cognitive impairment (MCI), and AD participants with standardized clinical assessments and structural neuroimaging measures were used. Twenty-six candidate proteins were quantified in 1148 subjects using multiplex (xMAP) assays. RESULTS: Sixteen proteins correlated with disease severity and cognitive decline. Strongest associations were in the MCI group with a panel of 10 proteins predicting progression to AD (accuracy 87%, sensitivity 85%, and specificity 88%). CONCLUSIONS: We have identified 10 plasma proteins strongly associated with disease severity and disease progression. Such markers may be useful for patient selection for clinical trials and assessment of patients with predisease subjective memory complaints.

In-depth profiling of the peripheral blood mononuclear cells proteome for clinical blood proteomics.

Peripheral blood mononuclear cells (PBMCs) are an easy accessible cellular part of the blood organ and, along with platelets, represent the only site of active gene expression in blood. These cells undergo immunophenotypic changes in various diseases and represent a peripheral source of monitoring gene expression and posttranslational modifications relevant to many diseases. Little is known about the source of many blood proteins and we hypothesise that release from PBMCs through active and passive mechanisms may account for a substantial part of the plasma proteome. The use of state-of-the-art proteomic profiling methods in PBMCs will enable minimally invasive monitoring of disease progression or response to treatment and discovery of biomarkers. To achieve this goal, detailed mapping of the PBMC proteome using a sensitive, robust, and quantitative methodological setup is required. We have applied an indepth gel-free proteomics approach using tandem mass tags (TMT), unfractionated and SCX fractionated PBMC samples, and LC-MS/MS with various modulations. This study represents a benchmark in deciphering the PBMC proteome as we provide a deep insight by identifying 4129 proteins and 25503 peptides. The identified proteome defines the scope that enables PBMCs to be characterised as cellular major biomarker pool within the blood organ.

Quantification of pancreatic cancer proteome and phosphorylome: indicates molecular events likely contributing to cancer and activity of drug targets.

OBJECTIVE: LC-MS/MS phospho-proteomics is an essential technology to help unravel the complex molecular events that lead to and propagate cancer. We have developed a global phospho-proteomic workflow to determine activity of signaling pathways and drug targets in pancreatic cancer tissue for clinical application. METHODS: Peptides resulting from tryptic digestion of proteins extracted from frozen tissue of pancreatic ductal adenocarcinoma and background pancreas (n = 12), were labelled with tandem mass tags (TMT 8-plex), separated by strong cation exchange chromatography, then were analysed by LC-MS/MS directly or first enriched for phosphopeptides using IMAC and TiO2, prior to analysis. In-house, commercial and freeware bioinformatic platforms were used to identify relevant biological events from the complex dataset. RESULTS: Of 2,101 proteins identified, 152 demonstrated significant difference in abundance between tumor and non-tumor tissue. They included proteins that are known to be up-regulated in pancreatic cancer (e.g. Mucin-1), but the majority were new candidate markers such as HIPK1 & MLCK. Of the 6,543 unique phosphopeptides identified (6,284 unique phosphorylation sites), 635 showed significant regulation, particularly those from proteins involved in cell migration (Rho guanine nucleotide exchange factors & MRCKα) and formation of focal adhesions. Activator phosphorylation sites on FYN, AKT1, ERK2, HDAC1 and other drug targets were found to be highly modulated (≥2 fold) in different cases highlighting their predictive power. CONCLUSION: Here we provided critical information enabling us to identify the common and unique molecular events likely contributing to cancer in each case. Such information may be used to help predict more bespoke therapy suitable for an individual case.

In vivo profiling of DPP4 inhibitors reveals alterations in collagen metabolism and accumulation of an amyloid peptide in rat plasma.

Dipeptidyl peptidase 4 (DPP4) inhibitors represent a novel class of oral anti-hyperglycemic agents. The complete pharmacological profile of these protease inhibitors remains unclear. In order to gain deeper insight into the in vivo effects caused by DPP4 inhibition, two different DPP4 inhibitors (vildagliptin and AB192) were analyzed using differential peptide display. Wistar rats were treated with the DPP4 inhibitors (0.3mgkg(-1); 1mgkg(-1) or 3mgkg(-1) body weight) and DPP4 activity was measured before and at the end of the experiment. One hour after compound administration, blood plasma samples were collected to generate peptide displays and to subsequently identify differentially regulated peptides. A dose-dependent decrease in blood plasma DPP4 activity was measured for both inhibitors. DPP4 inhibition influenced collagen metabolism leading to depletion of collagen derived peptides (e.g. collagen alpha 1 (III) 521-554) and accumulation of related N-terminally extended collagen derived peptides (e.g. collagen alpha 1 (III) 519-554). Furthermore, the intact amyloid rat BRI (1-23) peptide was detected in plasma following in vivo DPP4 inhibition. DPP4 catalyzed cleavage kinetics of the BRI peptide were determined in vitro. The k(cat) and K(m) for cleavage by DPP4 were 5.2s(-1) and 14microM, respectively, resulting in a specificity constant k(cat)/K(m) of 0.36 x 10(6)s(-1)M(-1). Our results demonstrate that differential peptide analysis can be applied to monitor action of DPP4 inhibition in blood plasma. For the first time effects on basal collagen metabolism following DPP4 inhibition in vivo were demonstrated and the BRI amyloid peptide was identified as a novel DPP4 substrate.

Performance of PLS regression coefficients in selecting variables for each response of a multivariate PLS for omics-type data.

Multivariate partial least square (PLS) regression allows the modeling of complex biological events, by considering different factors at the same time. It is unaffected by data collinearity, representing a valuable method for modeling high-dimensional biological data (as derived from genomics, proteomics and peptidomics). In presence of multiple responses, it is of particular interest how to appropriately "dissect" the model, to reveal the importance of single attributes with regard to individual responses (for example, variable selection). In this paper, performances of multivariate PLS regression coefficients, in selecting relevant predictors for different responses in omics-type of data, were investigated by means of a receiver operating characteristic (ROC) analysis. For this purpose, simulated data, mimicking the covariance structures of microarray and liquid chromatography mass spectrometric data, were used to generate matrices of predictors and responses. The relevant predictors were set a priori. The influences of noise, the source of data with different covariance structure and the size of relevant predictors were investigated. Results demonstrate the applicability of PLS regression coefficients in selecting variables for each response of a multivariate PLS, in omics-type of data. Comparisons with other feature selection methods, such as variable importance in the projection scores, principal component regression, and least absolute shrinkage and selection operator regression were also provided.

Peptidomics analysis of human blood specimens for biomarker discovery.

This review addresses the concepts, limitations and perspectives for the application of peptidomics science and technologies to discover putative biomarkers in blood specimens. Peptidomics can be defined as the comprehensive multiplex analysis of endogenous peptides contained within a biological sample under defined conditions to describe the multitude of native peptides in a biological compartment. In addition to the discovery of disease associated biomarkers, an emerging field in peptidomics is the analysis of peptides to describe in vivo effects of protease inhibitors. The development and application of peptidomics technologies represent an arena of biomarker research that has the potential for adding significant clinical value.

Oncopeptidomics--a commentary on opportunities and limitations.

Cancer cells exhibit specific changes in protein expression and alterations in proteolytic activities. Peptides are capable of reflecting these pathological changes and are educible by dedicated analytical technologies. Oncopeptidomics can be defined as the comprehensive multiplexed analysis of endogenous peptides from a biological sample, under defined conditions, to discover probable valid peptide tumor biomarker. Here, mass spectrometry has shown its potential as a comprehensive peptide profiling tool. The efforts to arrive at diagnostically relevant biomarkers may have been underestimated. The establishment of novel cancer biomarkers will necessitate a multidisciplinary effort and presumably require a duration comparable to the drug development process. This review will address current concepts, new perspectives and the developmental process leading to clinically useful peptide tumor markers.

Correlation-associated peptide networks of human cerebrospinal fluid.

Profiling of peptides and small proteins from either human body fluids or tissues by chromatography and subsequent mass spectrometry reveals several thousand individual peptide signals per sample. Any peptide is an intermediate in the course of biosynthesis, post-translational modification (PTM), proteolytic processing and degradation. Changes in the concentration of one peptide often affects the concentration of the other, hence a challenge consists in the development of suitable tools to turn this large amount of data into biologically relevant information. Comprehensive statistical analysis of the peptide profiling data allows associating peptides, which are closely related in terms of peptide biochemistry. Here, the bioinformatic concept of peptide networks, correlation-associated peptide networks (CANs), is introduced. Peptides with statistical similarity of their concentrations are grouped in form of networks, and these networks are interpreted in terms of peptide biochemistry. The spectrum of functional relationships found in cerebrospinal fluid CAN covers PTM and proteolytic degradation of peptides, clearance processing in the complement cascade, common secretion of peptides by neuroendocrine cells as well as ubiquitin-mediated degradation. Our results indicate that CAN is a powerful bioinformatic tool for the systematic analysis and interpretation of large peptidomics and proteomics data and helps to discover novel bioactive and diagnostic peptides.

The peptidomics concept.

Peptides are a paramount example of how nature diversifies from one single gene to release multiple, regulated functionalities at the desired sites and time. To achieve this, peptides are sequentially generated by a complex network of more than 500 proteases, acting at intracellular sites, upon secretion, in extracellular environments, and, finally, serving (regulated) degradation. This cycle of maturation, activation, and degradation points out that the peptidome is mechanistically linked to the proteome: the distribution between both is regulated by proteases and counter-regulated by protease inhibitors. Given the high diversity of peptides in living systems and their involvement in key regulatory processes, a need for improved peptide discovery, ideally combining peptide sequence identification with peptide profiling, has emerged. Standard proteomic approaches are not suitable for a systematic peptide analysis, since they do not cover the low molecular mass window. The new direction in proteomic research to analyse this "terra incognita" is peptidomics. This novel concept aims at the comprehensive visualization and analysis of small polypeptides, thus covering the mass range between proteomics and metabonomics. The pacemakers for the development of peptidomics technologies are modern mass spectrometry and bioinformatics. They are ideally suited for sensitive and comprehensive peptide analysis, especially in combination with the massive information content of today's genomic and transcriptomic databases. Given the high diversity of native peptides in living systems, clinical chemistry and modern medicine are the prime application areas. The discovery of relevant peptide biomarkers and drug targets will strongly benefit from peptidomics.

Datamining methodology for LC-MALDI-MS based peptide profiling.

This report will provide a brief overview of the application of data mining in proteomic peptide profiling used for medical biomarker research. Mass spectrometry based profiling of peptides and proteins is frequently used to distinguish disease from non-disease groups and to monitor and predict drug effects. It has the promising potential to enter clinical laboratories as a general purpose diagnostic tool. Data mining methodologies support biomedical science to manage the vast data sets obtained from these instrumentations. Here we will review the typical workflow of peptide profiling, together with typical data mining methodology. Mass spectrometric experiments in peptidomics raise numerous questions in the fields of signal processing, statistics, experimental design and discriminant analysis.

Prerequisites for peptidomic analysis of blood samples: II. Analysis of human plasma after oral glucose challenge -- a proof of concept.

Mass spectrometric plasma analysis for biomarker discovery has become an exploratory focus in proteomic research: the challenges of analyzing plasma samples by mass spectrometry have become apparent not only since the human proteome organization (HUPO) has put much emphasis on the human plasma proteome. This work demonstrates fundamental proteomic research to reveal sensitivity and quantification capabilities of our Peptidomics technologies by detecting distinct changes in plasma peptide composition in samples after challenging healthy volunteers with orally administered glucose. Differential Peptide Display (DPD) is a technique for peptidomics studies to compare peptides from distinct biological samples. Mass spectrometry (MS) is used as a qualitative and quantitative analysis tool without previous trypsin digestion or labeling of the samples. Circulating peptides (< 15 kDa) were extracted from 1.3 mL plasma samples and the extracts separated by liquid chromatography into 96 fractions. Each fraction was subjected to MALDI MS, and mass spectra of all fractions were combined resulting in a 2D-display of > 2,000 peptides from each sample. Endogenous peptides that responded to oral glucose challenge were detected by DPD of pre-and post-challenge plasma samples from 16 healthy volunteers and subsequently identified by nESI-qTOF MS. Two of the 15 MS peaks that were significantly modulated by glucose challenge were subsequently identified as insulin and C-peptide. These results were validated by using immunoassays for insulin and C-peptide. This paper serves as a proof of principle for proteomic biomarker discovery down to the pM concentration range by using small amounts of human plasma.

Towards characterization of the human urinary peptidome.

Biomarker discovery in human urine has become an evolving and potentially valuable topic in relation to renal function and diseases of the urinary tract. In order to deliver on the promises and to facilitate the development of validated biomarkers or biomarker panels, protein and peptide profiling techniques need high sample throughput, speed of analysis, and reproducibility of results. Here, we outline the performance characteristics of the liquid chromatography/MALDI-TOF-MS based differential peptide display (DPD(1)) approach for separating, detecting, abundance profiling and identification of native peptides derived from human urine. The typical complexity of peptides in human urine (resolution of the technique with respect to detectable number of peptides), the reproducibility (coefficient of variation for abundance profiles of all peptides detected in biological samples) and dynamic range of the technique as well as the lower limit of detection were characterized. A substantial number of peptides present in normal human urine were identified and compared to findings in four published proteome studies. In an explorative approach, pathological urines from patients suffering from post-renal-filtration diseases were qualitatively compared to normal urine. In conclusion, the peptidomics technology as shown here has a great potential for high throughput and high resolution urine peptide profiling analyses. It is a promising tool to study not only renal physiology and pathophysiology and to determine new biomarkers of renal diseases; it also has the potential to study remotely localized or systemic aberrations within human biology.